Reading Multi Spectral Images

https://nbviewer.jupyter.org/github/thomasaarholt/hyperspy-demos/blob/master/2_SVD_and_BSS.ipynb

Multispectral Imagery

Images obtained with a ADC Lite - Tetracam's Lightweight ADC

I made pitures about:

Aluminum , Copper, Brass, Iron, Stainless Steel, Painted Iron

http://tetracam.com/Products-ADC_Lite.htm

MRobalinho - 01-06-2019 Version 8

Add Libraries

# Add libraries
import glob, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from PIL import Image, ImageFilter, ImageOps
from openpyxl import load_workbook

# Clear all
os.system( 'cls' )

# Verify my current folder
currDir = os.path.dirname(os.path.realpath("__file__"))
mypath = currDir
print(currDir)  

C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook

# Path to the image files
folder = "imagedata08"

# Part name of file to filter files
end_file = ".TIF"

# Upper End File
#end_file = end_file.upper()
path = currDir + "/" + folder + "/"
end_file

'.TIF'

Read images from folder

# Read files from folder
print(path)
print('-')
print(' ---- IMAGES ON THE FOLDER :', folder, '------- *', end_file)

list_of_images = list()  # save all images on folder for further processing 

for file in os.listdir(path):
    if file.endswith(end_file):
        print(os.path.join(file))
        list_of_images.append(file)   # save all images on folder for further processing 
print('-')        

C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/
-
 ---- IMAGES ON THE FOLDER : imagedata08 ------- * .TIF
Aluminum_01.TIF
Aluminum_02.TIF
Aluminum_03.TIF
Aluminum_04.TIF
Brass_01.TIF
Brass_02.TIF
Brass_04.TIF
Brass_05.TIF
Brass_06.TIF
Brass_07.TIF
Brass_08.TIF
CopperWire_01.TIF
CopperWire_02.TIF
CopperWire_03.TIF
CopperWire_04.TIF
CopperWire_05.TIF
Copper_01.TIF
Copper_02.TIF
Copper_03.TIF
Copper_04.TIF
Copper_05.TIF
Copper_06.TIF
Copper_07.TIF
Copper_08.TIF
Enh_Aluminum_01.TIF
Enh_Aluminum_02.TIF
Enh_Aluminum_03.TIF
Enh_Aluminum_04.TIF
Enh_Brass_01.TIF
Iron_01.TIF
Iron_02.TIF
Iron_03.TIF
Iron_04.TIF
Iron_05.TIF
Iron_06.TIF
Iron_07.TIF
Iron_08.TIF
MASK_Enh_Aluminum_01.TIF
MASK_Enh_Aluminum_02.TIF
MASK_Enh_Aluminum_03.TIF
MASK_Enh_Aluminum_04.TIF
MASK_Enh_Brass_01.TIF
PaintedIron_01.TIF
PaintedIron_02.TIF
PaintedIron_03.TIF
PaintedIron_04.TIF
PaintedIron_05.TIF
PaintedIron_06.TIF
StainlessSteel_01.TIF
StainlessSteel_02.TIF
StainlessSteel_03.TIF
StainlessSteel_04.TIF
StainlessSteel_05.TIF
StainlessSteel_06.TIF
-

# Create Data Frame with image information
df_image = []

Functions to the work

# Read image with PIL
from PIL import Image, ImageFilter, ImageOps
def read_pil_image(file1):
    #print('Reading PIL image:', file1)
    try:
        im_pil = Image.open(file1)
    except:
        print("-->Unable to load image",file1)
    return im_pil

# Read image with OPENCV
import cv2
def read_cv2_image(file1):
    #print('Reading CV image:',file1)
    try:
        im_cv = cv2.imread(file1)
    except:
        print("-->Unable to load image",file1)
    return im_cv

# Look from an chanel from then image

def channel(img, n):
    """Isolate the nth channel from the image.

       n = 0: red, 1: green, 2: blue
    """
    a = np.array(img)
    a[:,:,(n!=0, n!=1, n!=2)] *= 0
#   a[:,:,n] *= 0
#   print(Image.fromarray(a), 'Get Channel n: ', n)

    print('Get Channel n: ', n)
    return Image.fromarray(a)

# def to resize 
# Given parameters : image , number to divide (resize)
def imageResize(img, n):
    width, height = img.size 

    print('Original size:', width, '/', height, 'Resize:',n)
    
    newWidth = int(width / n)
    newHeight = int(height / n)
    img.resize((newWidth, newHeight), Image.ANTIALIAS)
    print('New size:', newWidth, '/', newHeight)
    return img

# Obtain main color from image
# https://convertingcolors.com/rgb-color-169_171_170.html
    
def get_main_color(path, file):
    #img = Image.open(path+file)
    file1 = path+file
    # Read image
    img = read_pil_image(file1)
    if img == None:
        print("-->Unable to load image",file1)
        
    colors = img.getcolors( 1024*1024) #put a higher value if there are many colors in your image
    print('Get main Color file:', file)
    max_occurence, most_present = 0, 0
    try:
        for c in colors:
            if c[0] > max_occurence:
                (max_occurence, most_present) = c
        return most_present
    except TypeError:
        raise Exception("Too many colors in the image")

#!/usr/bin/python

# Return one 24-bit color value 
def rgbToDecimal(x_rgb):
    r,g,b = rgbToRGB(x_rgb)
    rgb_dec = (r << 16) + (g << 8) + b
    #print('RGB Color:', x_rgb, '   Dec:', rgb_dec)
    return rgb_dec

# Convert 24-bit color value to RGB
def colorToRGB(c):
    r = c >> 16
    c -= r * 65536;
    g = c / 256
    c -= g * 256;
    b = c
    return [r, g, b]

def rgbToRGB(x_rgb):
    x_rgb = list(x_rgb)
    r = x_rgb[0]
    g = x_rgb[1]
    b = x_rgb[2]

    #print('rgbToRGB:',x_rgb, r,g,b)
    return r, g, b

def getRGBfromI(RGBint):
    blue =  RGBint & 255
    green = (RGBint >> 8) & 255
    red =   (RGBint >> 16) & 255
    return red, green, blue

def getIfromRGB(rgb):
    red = rgb[0]
    green = rgb[1]
    blue = rgb[2]
    #print('getIfromRGB:', red, green, blue)
    RGBint = (red<<16) + (green<<8) + blue
    return RGBint

# RGB to Hex Decimal
def rgb_to_hex(rgb):
    rgb_int = bytes(rgb).hex()
    rgb_dec = '#'+str(rgb_int)
    #print('RGB :',rgb, '  Hex Dec:', rgb_dec)
    return rgb_dec

# Test
#x_rgb = (254, 250, 255)
#rgb_hex = rgb_to_hex(x_rgb)
#rgb_dec = rgbToDecimal(x_rgb)

# https://github.com/conda-forge/webcolors-feedstock
# conda config --add channels conda-forge
# conda install webcolors
# It is possible to list all of the versions of webcolors available on your platform with:
#       conda search webcolors --channel conda-forge

# COLOR NAME
import webcolors
def get_color_name(rgb_x):
    min_colours = {}
    for key, name in webcolors.css21_hex_to_names.items():
        r_c, g_c, b_c = webcolors.hex_to_rgb(key)
        rd = (r_c - rgb_x[0]) ** 2
        gd = (g_c - rgb_x[1]) ** 2
        bd = (b_c - rgb_x[2]) ** 2
        min_colours[(rd + gd + bd)] = name
    print('Color name from RGB:',rgb_x,'  is :',min_colours[min(min_colours.keys())])
    return min_colours[min(min_colours.keys())]

# Get color name from RGB
# https://stackoverflow.com/questions/2453344/find-the-colour-name-from-a-hexadecimal-colour-code

colorof = {'#F0F8FF':"aliceblue",
'#FAEBD7':"antiquewhite",
'#00FFFF':"aqua",
'#7FFFD4':"aquamarine",
'#F0FFFF':"azure",
'#F5F5DC':"beige",
'#FFE4C4':"bisque",
'#000000':"black",
'#FFEBCD':"blanchedalmond",
'#0000FF':"blue",
'#8A2BE2':"blueviolet",
'#A52A2A':"brown",
'#DEB887':"burlywood",
'#5F9EA0':"cadetblue",
'#7FFF00':"chartreuse",
'#D2691E':"chocolate",
'#FF7F50':"coral",
'#6495ED':"cornflowerblue",
'#FFF8DC':"cornsilk",
'#DC143C':"crimson",
'#00FFFF':"cyan",
'#00008B':"darkblue",
'#008B8B':"darkcyan",
'#B8860B':"darkgoldenrod",
'#A9A9A9':"darkgray",
'#006400':"darkgreen",
'#BDB76B':"darkkhaki",
'#8B008B':"darkmagenta",
'#556B2F':"darkolivegreen",
'#FF8C00':"darkorange",
'#9932CC':"darkorchid",
'#8B0000':"darkred",
'#E9967A':"darksalmon",
'#8FBC8B':"darkseagreen",
'#483D8B':"darkslateblue",
'#2F4F4F':"darkslategray",
'#00CED1':"darkturquoise",
'#9400D3':"darkviolet",
'#FF1493':"deeppink",
'#00BFFF':"deepskyblue",
'#696969':"dimgray",
'#1E90FF':"dodgerblue",
'#B22222':"firebrick",
'#FFFAF0':"floralwhite",
'#228B22':"forestgreen",
'#FF00FF':"fuchsia",
'#DCDCDC':"gainsboro",
'#F8F8FF':"ghostwhite",
'#FFD700':"gold",
'#DAA520':"goldenrod",
'#808080':"gray",
'#008000':"green",
'#ADFF2F':"greenyellow",
'#F0FFF0':"honeydew",
'#FF69B4':"hotpink",
'#CD5C5C':"indianred",
'#4B0082':"indigo",
'#FFFFF0':"ivory",
'#F0E68C':"khaki",
'#E6E6FA':"lavender",
'#FFF0F5':"lavenderblush",
'#7CFC00':"lawngreen",
'#FFFACD':"lemonchiffon",
'#ADD8E6':"lightblue",
'#F08080':"lightcoral",
'#E0FFFF':"lightcyan",
'#FAFAD2':"lightgoldenrodyellow",
'#D3D3D3':"lightgray",
'#90EE90':"lightgreen",
'#FFB6C1':"lightpink",
'#FFA07A':"lightsalmon",
'#20B2AA':"lightseagreen",
'#87CEFA':"lightskyblue",
'#778899':"lightslategray",
'#B0C4DE':"lightsteelblue",
'#FFFFE0':"lightyellow",
'#00FF00':"lime",
'#32CD32':"limegreen",
'#FAF0E6':"linen",
'#FF00FF':"magenta",
'#800000':"maroon",
'#66CDAA':"mediumaquamarine",
'#0000CD':"mediumblue",
'#BA55D3':"mediumorchid",
'#9370DB':"mediumpurple",
'#3CB371':"mediumseagreen",
'#7B68EE':"mediumslateblue",
'#00FA9A':"mediumspringgreen",
'#48D1CC':"mediumturquoise",
'#C71585':"mediumvioletred",
'#191970':"midnightblue",
'#F5FFFA':"mintcream",
'#FFE4E1':"mistyrose",
'#FFE4B5':"moccasin",
'#FFDEAD':"navajowhite",
'#000080':"navy",
'#FDF5E6':"oldlace",
'#808000':"olive",
'#6B8E23':"olivedrab",
'#FFA500':"orange",
'#FF4500':"orangered",
'#DA70D6':"orchid",
'#EEE8AA':"palegoldenrod",
'#98FB98':"palegreen",
'#AFEEEE':"paleturquoise",
'#DB7093':"palevioletred",
'#FFEFD5':"papayawhip",
'#FFDAB9':"peachpuff",
'#CD853F':"peru",
'#FFC0CB':"pink",
'#DDA0DD':"plum",
'#B0E0E6':"powderblue",
'#800080':"purple",
'#FF0000':"red",
'#BC8F8F':"rosybrown",
'#4169E1':"royalblue",
'#8B4513':"saddlebrown",
'#FA8072':"salmon",
'#F4A460':"sandybrown",
'#2E8B57':"seagreen",
'#FFF5EE':"seashell",
'#A0522D':"sienna",
'#C0C0C0':"silver",
'#87CEEB':"skyblue",
'#6A5ACD':"slateblue",
'#708090':"slategray",
'#FFFAFA':"snow",
'#00FF7F':"springgreen",
'#4682B4':"steelblue",
'#D2B48C':"tan",
'#008080':"teal",
'#D8BFD8':"thistle",
'#FF6347':"tomato",
'#40E0D0':"turquoise",
'#EE82EE':"violet",
'#F5DEB3':"wheat",
'#FFFFFF':"white",
'#F5F5F5':"whitesmoke",
'#FFFF00':"yellow",
'#9ACD32':"yellowgreen"}


def get_rgb_color_name(rgb):
    
    hex_from_rgb = rgb_to_hex(rgb)  # transform RGB into hexadecimal
    hx = hex_from_rgb[1:8]
    #print(hx)
    # if color is found in dict
    if colorof.get(hx):return colorof[hx]

    # else return its closest available color
    m = 16777215
    k = '000000'
    for key in colorof.keys():
        key_color = key[1:8]
        #print(key_color)
        a = int(hx[:2],16)-int(key_color[:2],16)
        b = int(hx[2:4],16)-int(key_color[2:4],16)
        c = int(hx[4:],16)-int(key_color[4:],16)

        v = a*a+b*b+c*c # simple measure for distance between colors

        # v = (r1 - r2)^2 + (g1 - g2)^2 + (b1 - b2)^2

        if v <= m:
            m = v
            k = key

    return colorof[k], hex_from_rgb

# Test
#rgb_1 = (216, 220, 223)
#cname, hexdc = get_rgb_color_name(rgb_1)
#print('Found:',    cname, '  Hex:', hexdc)     # found in dict

# Increase the contrast image
# im - image
# xvalue = contrast value
# https://pillow.readthedocs.io/en/4.0.x/reference/ImageEnhance.html
from PIL import ImageEnhance
# Path + file name + numeric value to enhancement

def contrast(path, xfile, xvalue):
    print('   Enhance image:', xfile, '  Value:', xvalue)
    file1 = path + xfile
    # Read Image
    im = read_pil_image(file1)
    if im == None:
        print("-->Unable to load image",file1)
    
    enh = ImageEnhance.Contrast(im)
    # enh.enhance(1.0).show("30% more contrast")
    x_enh = enh.enhance(xvalue)
     # Create name file masked
    f2_file = 'Enh_' + xfile
    print('   Save enhanced file :', f2_file)
    x_enh.save(f2_file)  # save enhanced file
    return x_enh, f2_file

# Return RGB separately
def return_rgb_from_RGB(rgb):
    p_rgb = list(rgb)
    red   = p_rgb[0]
    green = p_rgb[1]
    blue  = p_rgb[2]
    return red, green, blue

# Return distance from 2 colors

# http://hanzratech.in/2015/01/16/color-difference-between-2-colors-using-python.html
# https://python-colormath.readthedocs.io/en/latest/delta_e.html#delta-e-cie-2000

from colormath.color_objects import sRGBColor, LabColor
from colormath.color_conversions import convert_color
from colormath.color_diff import delta_e_cie2000

def delta_2_colors(rgb_1, rgb_2):
    #print('   Delta colors: ', rgb_1, rgb_2)
    #---- first color
    xr, xg, xb = return_rgb_from_RGB(rgb_1)
    # Red Color
    color1_rgb = sRGBColor(xr, xg, xb)

    #--- other color
    rgb_1 = rgb_2
    xr, xg, xb = return_rgb_from_RGB(rgb_1)
    # Blue Color
    color2_rgb = sRGBColor(xr, xg, xb)

    # Convert from RGB to Lab Color Space
    color1_lab = convert_color(color1_rgb, LabColor)

    # Convert from RGB to Lab Color Space
    color2_lab = convert_color(color2_rgb, LabColor)

    # Find the color difference
    delta_e = delta_e_cie2000(color1_lab, color2_lab)

    #print("      The difference between the 2 color = ", delta_e)
    return delta_e

# Remove Background - Put red background
#https://stackoverflow.com/questions/29313667/how-do-i-remove-the-background-from-this-kind-of-image
    
import cv2
import numpy as np

def red_background(path, xfile):
    print('   Red background for image:', xfile)
    #== Parameters =======================================================================
    BLUR = 21
    CANNY_THRESH_1 = 10
    CANNY_THRESH_2 = 100
    MASK_DILATE_ITER = 10
    MASK_ERODE_ITER = 10
    MASK_COLOR = (0.0,0.0,1.0) # In BGR format

    #== Processing =======================================================================
    file1 = path + xfile
    #-- Read image -----------------------------------------------------------------------
    #img = cv2.imread(file1)
    # Read image
    img = read_cv2_image(file1)
    if img.any() == None:
        print("-->Unable to load image",file1)
    
    # Create GRAY Image
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    #-- Edge detection -------------------------------------------------------------------
    edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)
    edges = cv2.dilate(edges, None)
    edges = cv2.erode(edges, None)

    #-- Find contours in edges, sort by area ---------------------------------------------
    contour_info = []
    _, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
    for c in contours:
        contour_info.append((
            c,
            cv2.isContourConvex(c),
            cv2.contourArea(c),
        ))
    contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
    max_contour = contour_info[0]

    #-- Create empty mask, draw filled polygon on it corresponding to largest contour ----
    # Mask is black, polygon is white
    mask = np.zeros(edges.shape)
    for c in contour_info:
        cv2.fillConvexPoly(mask, c[0], (255))

    #-- Smooth mask, then blur it
    mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)
    mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)
    mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)
    mask_stack = np.dstack([mask]*3)    # Create 3-channel alpha mask

    #-- Blend masked img into MASK_COLOR background
    mask_stack  = mask_stack.astype('float32') / 255.0         
    img         = img.astype('float32') / 255.0    
    masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR)  
    masked = (masked * 255).astype('uint8')                    
    
    cv2.imwrite(path+"MASK_"+xfile,masked)
    
    # Create name file masked
    f2_file = 'Mask_'+ xfile
    file2 = path + f2_file
    
    # Write masked image on disk
    print('   Save masked image with red background:', f2_file)
    cv2.imwrite(file2, masked)           # Save
    # Return name file masked and image masked
    return f2_file, masked

# Test
'''
xfile = 'Brass_001.tif'
f2_file, masked = red_background(path,xfile)
%matplotlib inline
plt.imshow(masked)
plt.title('Remove image background:'+xfile,fontsize=20)
plt.show()
'''

"\nxfile = 'Brass_001.tif'\nf2_file, masked = red_background(path,xfile)\n%matplotlib inline\nplt.imshow(masked)\nplt.title('Remove image background:'+xfile,fontsize=20)\nplt.show()\n"

# https://convertingcolors.com/rgb-color-169_171_170.html

# return most_present RGB, RGB, color name, list RGB colors without RED, list RGB colors without back

import collections

def get_main_color_without_red_and_floor(path, f2_file):
    print('    Main color from image:', f2_file)
    file1 = path + f2_file
    
    # Read image
    img = read_pil_image(file1)
    if img == None:
        print("-->Unable to load image",file1)
        
    colors = img.getcolors( 1024*1024) #put a higher value if there are many colors in your image
    #-----
    # Create list with colors without Background red color (near Background color)
    list_non_back = list()
    list_dec_back = list()   # List from decimal colors to list_non_back
    #
    print('...  List without excluded colors')
    # Convert list to decimal color
    for color in colors:
        # Diference between colors
       # print(color[1])
        rgb = color[1]

        excluded_rgb = False
        
        #Verify color name
        xt_color_name , hexdc = get_rgb_color_name(rgb)
        
        # Exclusion for some colors (Red Backgroud, Black foor, etc)
        if "red"   in xt_color_name: 
             excluded_rgb = True
        if "black" in xt_color_name:        
             excluded_rgb = True
        if "white" in xt_color_name:
            excluded_rgb = True
        if "cream" in xt_color_name:
            excluded_rgb = True             
        
        # Force Only for non-tif files we do not delete anything
        if file.endswith('.tif'):
            excluded_rgb = False
    
        if  excluded_rgb == True:     # Exclude COLOR  
            #print("Cor excluida", rgb, xt_color_name )
            excluded_rgb = True
        else:
            # OK COLOR - Save color in the list of correct colors (list_non_back)
            #print("Cor OK", rgb, xt_color_name )
            list_non_back.append(rgb)
            # Decimal color
            rgb_dec    = rgbToDecimal(rgb)
            list_dec_back.append(rgb_dec)   
  
    #-----
    print('Count ocurrencies for color')
    most_present = 0

    # Most common color in the list - list_non_back
    x = collections.Counter(list_non_back)
    print('      4 Most common colors:', x.most_common(4))  # Five most common colors
    most_present = x.most_common(1)
    xrgb = list_non_back[0] # common color
    
    # ----- color name --
    #xt_color_name = get_color_name(xrgb)
    print('      Read color name:', xrgb)  # Color name from RGB
    xt_color_name , hexdc = get_rgb_color_name(xrgb)
    print('      Main Color file:', f2_file, ' RGB:', most_present, xrgb, ' Color name:', xt_color_name,' Hex:',hexdc)
    
    return most_present, xrgb, xt_color_name, list_non_back, list_dec_back

# Test
#xfile = 'Copper_001.tif'
#most_present, xrgb, xt_color_name, list_non_back, \
#     list_dec_back = get_main_color_without_red_and_floor(path, xfile)

# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.html
# Print histogram using Opencv
import cv2
import numpy as np
from matplotlib import pyplot as plt

def print_cv_hist(path, xfile):
    file1 = path + xfile
    print('Cv2 Hist from file:', file1)
    
    # Read image
    img_cv = read_cv2_image(file1)
    if img_cv.any() == None:
        print("-->Unable to load image",file1)

    # create a mask
    mask = np.zeros(img_cv.shape[:2], np.uint8)

    # define area to extract image from original
    #    Left:height , right:length
    mask[200:1400, 200:1800] = 255
    masked_img = cv2.bitwise_and(img_cv, img_cv ,mask = mask)

    # Calculate histogram with mask and without mask
    # Check third argument for mask
    hist_full = cv2.calcHist([img_cv],[0],None,[256],[0,256])
    hist_mask = cv2.calcHist([img_cv],[0],mask,[256],[0,256])

    plt.figure(figsize=(18,5))

    plt.subplot(141), plt.imshow(img_cv, 'gray')
    plt.title("Original")

    plt.subplot(142), plt.imshow(mask,'gray')
    plt.title('Mask')

    plt.subplot(143), plt.imshow(masked_img, 'gray')
    plt.title('Masked image')


    ax=plt.subplot(144), plt.plot(hist_full), plt.plot(hist_mask)
    ax = plt.gca()
    ax.grid(True)
    plt.title('Histogram')
    plt.xlim([0,256])

    plt.suptitle('IMAGE HISTOGRAM',fontsize=18)
    plt.xlabel('Image:'+xfile,fontsize=18)
    plt.ylabel('All chanels',fontsize=10)
    plt.savefig(path+'Hist_cv2_'+xfile)   # Save Histograme Figure
    plt.show()
    return

# Test
#xfile = 'Copper_001.tif'
#print_cv_hist(path, xfile)

# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.html
# Print histogram using Opencv and matplotlib

import cv2
import numpy as np
from matplotlib import pyplot as plt

def print_matplot_hist(path, xfile):
    file1 = path + xfile   
    print('Matplot Hist from file:', file1)
    
    # Read image
    img_mp = read_cv2_image(file1)
    if img_mp.any() == None:
        print("-->Unable to load image",file1)
    
    color = ('b','g','r')
    ax = plt.figure(figsize=(10,5))
    ax = plt.gca()
    ax.grid(True)
    
    for i,col in enumerate(color):
        histr = cv2.calcHist([img_mp],[i],None,[256],[0,256])
        plt.plot(histr,color = col, label='Band '+col.upper())
        plt.xlim([0,256])

    plt.title('Histogram of the image',fontsize=20)
    plt.xlabel('Image:'+xfile,fontsize=18)
    plt.ylabel('All chanels',fontsize=18)
    plt.legend(bbox_to_anchor=(.90,0.85),bbox_transform=plt.gcf().transFigure)
    plt.savefig(path+'Hist_'+xfile)   # Save Histograme Figure
    plt.show()   
        
    return

# Test
#xfile = 'Copper_1.tif'
#print_matplot_hist(path, xfile)

# Max and Min value from Histogram and each position
#l = np.array(hist_full).tolist()  - Transform array in a list

import cv2
import numpy as np
from matplotlib import pyplot as plt

def histogram_max_min(path, xfile):

    file1 = path+xfile
    print('Histogram analisys:', file1)
    
    # Read image
    imgh = read_cv2_image(file1)
    if imgh.any() == None:
        print("-->Unable to load image",file1)

    # Calculate histogram without mask
    hist_full = cv2.calcHist([imgh],[0],None,[256],[0,256])
 
    # Transform array in a list
    hist_list = np.array(hist_full).tolist()

    # Valor maximo e minimo do Histograma e sua posição
    val_max  = max(hist_list)
    xval_max = int(val_max[0])
 
    val_avg  =  max(hist_list) 
    xval_avg = int(val_avg[0]) / len(hist_list)
    xval_avg = int(xval_avg)
    
    val_min  = min(hist_list)
    xval_min = int(val_min[0])
    
    idx_max = hist_list.index(val_max)
    idx_min = hist_list.index(val_min)
    
    #print("Valor Max Histograma:", xval_max, '  Posição do valor Max:', idx_max)
    #print("Valor Min Histograma:", xval_min, '  Posição do valor Min:', idx_min)
    #print("Valor Avg Histograma:", xval_avg)
  
    return xval_max, idx_max, xval_min, idx_min

# Test
#xfile = 'Copper_001.tif'
#_,_,_,_ = histogram_max_min(path, xfile)
 

# Read image folder
import glob, os
def get_image_folder(xfile1):
    # Path to the image files
    path = currDir + "/" + folder + "/"
    # File
    file1 = path + xfile1
    print(file1)
    
    return file1

# Obtain percentage of channels R,G,B
import matplotlib.image as mpimg
def percent_rgb(path, xfile):
    print('    RGB percent from image:', xfile)
    emptyBlue = []
    emptyGreen= []
    emptyRed= []
    
    all_path = path + xfile
    # Read file
    img = mpimg.imread(all_path)
    imgplot = plt.imshow(img)
    # Mean of the array of each chanel
    RGBtuple = np.array(img).mean(axis=(0,1))

    averageRed = RGBtuple[0]
    averageGreen = RGBtuple[1]
    averageBlue = RGBtuple[2]

    percentageGreen = averageGreen/(averageRed+averageGreen+averageBlue) * 100
    percentageBlue = averageBlue/(averageRed+averageGreen+averageBlue) * 100
    percentageRed = averageRed/(averageRed+averageGreen+averageBlue) * 100

    emptyBlue+=[percentageBlue]
    emptyGreen+=[percentageGreen]
    emptyRed+=[percentageRed]
    print('     ------------------------------')
    print('     Percent Red',percentageRed)
    print('     Percent Green',percentageGreen)
    print('     Percent Blue',percentageBlue)
    print('     ------------------------------')
    return percentageRed, percentageGreen, percentageBlue

# Print all the informations from image, and create a pandas data frame with the relevant information

def print_file(path, xfile):
    print('------------------------------------------------------------------------')   
    file1 = path + xfile
    
    # Read image
    tif_f1 = read_pil_image(file1)
    if tif_f1 == None:
        print("-->Unable to load image",file1)

    print('Inf.File:',xfile)

    # Transform Image to array
    aArray = np.array(tif_f1)
    # Array sum  
    xsum = aArray.sum() / 1000000
    
    # Get channel 0
    x0_channel = channel(tif_f1, 0)
    aArray = np.array(x0_channel)
    xsum_0 = aArray.sum() / 1000000  

    # Get channel 1
    x1_channel = channel(tif_f1, 1)
    aArray = np.array(x1_channel)
    xsum_1 = aArray.sum() / 1000000  

    # Get channel 2
    x2_channel = channel(tif_f1, 2)
    aArray = np.array(x2_channel)
    xsum_2 = aArray.sum() / 1000000  

    # Histogram from image
    aHist = tif_f1.histogram()
    hsum = sum(aHist) / 100000

    # Histogram channel 0
    aHist_0 = x0_channel.histogram()
    hsum_0 = sum(aHist_0) / 100000

    # Histogram channel 1
    aHist_1 = x1_channel.histogram()
    hsum_1 = sum(aHist_1) / 100000

    # Histogram chanel 0
    aHist_2 = x2_channel.histogram()
    hsum_2 = sum(aHist_2) / 100000

    # number elements on list
    nlist = len(aHist)
    
    # Max and Min from Histogram
    xval_max, idx_max, xval_min, idx_min = histogram_max_min(path, xfile)
    
    # Percentage RGB
    perc_R, perc_G, perc_B = percent_rgb(path, xfile)

    # Get color
    # Enhancement Contrast color for better definition
    # f1_file has the file name saved enhanced  
    xvalue = 2.0  
    print('Enhancement color:', xfile, '  Value:',xvalue)  
    x_enh, f1_file = contrast(path, xfile, xvalue)

    # Remove Background - Put red background
    # f2_file has the file name saved masked
    
    # Only red Background for NON tif files
    #xend_file = file.endswith('*.TIF').upper()
    if file.endswith('*.TIF'):
        f2_file = f1_file
        img_masked = tif_f1
    else:    
        file1 = path+f1_file  
        print('Red background:', path, f1_file)   
        f2_file, img_masked = red_background(path, f1_file)

    # Get Main Color - 
    print('Most common color:', path, f2_file)  
    
    # most present color, RGB from most present color:
    # color name , Hex from rgb , list colors withour red, list colors without back, decimal list colors without back
    most_present, xrgb, xt_color_name, list_non_back,list_dec_back = get_main_color_without_red_and_floor(path, f2_file) 
    
    # HEX fom most present color
    hex_color  = rgb_to_hex(xrgb)
    
    # Decimal from most present color
    rgb_dec    = rgbToDecimal(xrgb)
    #----
    # Get Extrems of the image
    extr_a = tif_f1.getextrema() 
    # Transform tuple in a list    
    extr_b = [x for sets in extr_a for x in sets]
    # Sum the list  
    sum_list = sum(extr_b) 
    med_extr  = sum_list / len(extr_b)
    #print('List Extremes:',extr_a,'Sum:',sum_list,'Len:', len(extr_b), 'Med:',med_extr)


    # Obtain name file without extension 
    sample_name = os.path.basename(xfile).split('_')[0]

    # Print information  
    print(sample_name,' Size:',tif_f1.size, ' Format:',tif_f1.format, ' Mode:', tif_f1.mode)
    print('          Sum array:',xsum, ' Sum Ch 0:', xsum_0, ' Sum Ch 1:', xsum_1, ' Sum Ch 2:', xsum_2)      
    print('          Histog   :',hsum ,'  N.List elem:', nlist, ' Max:', xval_max, 'Idx Max:', idx_max, '  Min:', xval_min, 'Idx Min:', idx_min )
    print('          Color    :',xt_color_name,'   RGB   :',xrgb, '   Hex color:', hex_color,'  Dec Color:',rgb_dec)
    print('          Extremes :',extr_a, 'Med Extremes:',med_extr)
    print('          Percentage R:', perc_R,'  Percentage G:', perc_B, '  Percentage B:', perc_B)

    # insert information in a Pandas Data Frame
    df_image.append((folder, xfile, sample_name, tif_f1.size, tif_f1.format, tif_f1.mode , 
                       xsum, xsum_0, xsum_1, xsum_2, hsum, nlist, xt_color_name, xrgb, hex_color, 
                       rgb_dec, med_extr, xval_max, idx_max, xval_min, idx_min,
                       perc_R, perc_G, perc_B))
    
    return most_present, xrgb, xt_color_name, list_non_back, list_dec_back
 

Starting image analysis

# Timer
import datetime
master_time_start = datetime.datetime.now() # global timer start

# Create Data Frame with image information
df_image = []

xend_file = "*" + end_file
# change work to folder path
os.chdir(path)
print('Analysing Images from:',path, xend_file)

for file in glob.glob(xend_file):
    file_time_start = datetime.datetime.now() # timer start
    list_dec_back = list() # List with decimal colors in the image
    print(file)
    most_present, xrgb, xt_color_name, list_non_back, list_dec_back = print_file(path,file)
    file_time_end = datetime.datetime.now()
    elapsed = file_time_end - file_time_start
    print('File Start:', file_time_start, ' Finish:', file_time_end, '  File Time:',elapsed) # timer end
    
master_time_end = datetime.datetime.now() # global timer end
elapsed = master_time_end - master_time_start
print('Process Start:', master_time_start, ' Finish:', master_time_end, '  Global Time:',elapsed)

Analysing Images from: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ *.TIF
Aluminum_01.TIF
------------------------------------------------------------------------
Inf.File: Aluminum_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_01.TIF
    RGB percent from image: Aluminum_01.TIF
     ------------------------------
     Percent Red 34.04091712429833
     Percent Green 34.04091712429833
     Percent Blue 31.918165751403325
     ------------------------------
Enhancement color: Aluminum_01.TIF   Value: 2.0
   Enhance image: Aluminum_01.TIF   Value: 2.0
   Save enhanced file : Enh_Aluminum_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Aluminum_01.TIF
   Red background for image: Enh_Aluminum_01.TIF
   Save masked image with red background: Mask_Enh_Aluminum_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Aluminum_01.TIF
    Main color from image: Mask_Enh_Aluminum_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((246, 227, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_Aluminum_01.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 225.341887  Sum Ch 0: 76.708445  Sum Ch 1: 76.708445  Sum Ch 2: 71.924997
          Histog   : 94.37184   N.List elem: 768  Max: 251838 Idx Max: 9   Min: 0 Idx Min: 208
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 218), (1, 218), (0, 218)) Med Extremes: 109.33333333333333
          Percentage R: 34.04091712429833   Percentage G: 31.918165751403325   Percentage B: 31.918165751403325
File Start: 2019-06-01 18:34:39.139760  Finish: 2019-06-01 18:34:53.218267   File Time: 0:00:14.078507
Aluminum_02.TIF
------------------------------------------------------------------------
Inf.File: Aluminum_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_02.TIF
    RGB percent from image: Aluminum_02.TIF
     ------------------------------
     Percent Red 33.995144701377555
     Percent Green 33.995144701377555
     Percent Blue 32.009710597244876
     ------------------------------
Enhancement color: Aluminum_02.TIF   Value: 2.0
   Enhance image: Aluminum_02.TIF   Value: 2.0
   Save enhanced file : Enh_Aluminum_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Aluminum_02.TIF
   Red background for image: Enh_Aluminum_02.TIF
   Save masked image with red background: Mask_Enh_Aluminum_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Aluminum_02.TIF
    Main color from image: Mask_Enh_Aluminum_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((232, 227, 223), 1), ((230, 227, 223), 1), ((228, 227, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Aluminum_02.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 232.768793  Sum Ch 0: 79.130088  Sum Ch 1: 79.130088  Sum Ch 2: 74.508617
          Histog   : 94.37184   N.List elem: 768  Max: 244041 Idx Max: 11   Min: 0 Idx Min: 211
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 212), (1, 212), (0, 211)) Med Extremes: 106.16666666666667
          Percentage R: 33.995144701377555   Percentage G: 32.009710597244876   Percentage B: 32.009710597244876
File Start: 2019-06-01 18:34:53.218267  Finish: 2019-06-01 18:35:07.965032   File Time: 0:00:14.746765
Aluminum_03.TIF
------------------------------------------------------------------------
Inf.File: Aluminum_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_03.TIF
    RGB percent from image: Aluminum_03.TIF
     ------------------------------
     Percent Red 33.884801040435796
     Percent Green 33.884801040435796
     Percent Blue 32.230397919128414
     ------------------------------
Enhancement color: Aluminum_03.TIF   Value: 2.0
   Enhance image: Aluminum_03.TIF   Value: 2.0
   Save enhanced file : Enh_Aluminum_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Aluminum_03.TIF
   Red background for image: Enh_Aluminum_03.TIF
   Save masked image with red background: Mask_Enh_Aluminum_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Aluminum_03.TIF
    Main color from image: Mask_Enh_Aluminum_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((250, 228, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_Aluminum_03.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 281.688117  Sum Ch 0: 95.449458  Sum Ch 1: 95.449458  Sum Ch 2: 90.789201
          Histog   : 94.37184   N.List elem: 768  Max: 246108 Idx Max: 11   Min: 0 Idx Min: 247
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 251), (1, 251), (0, 250)) Med Extremes: 125.66666666666667
          Percentage R: 33.884801040435796   Percentage G: 32.230397919128414   Percentage B: 32.230397919128414
File Start: 2019-06-01 18:35:07.965032  Finish: 2019-06-01 18:35:22.570715   File Time: 0:00:14.605683
Aluminum_04.TIF
------------------------------------------------------------------------
Inf.File: Aluminum_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_04.TIF
    RGB percent from image: Aluminum_04.TIF
     ------------------------------
     Percent Red 33.8613375326682
     Percent Green 33.8613375326682
     Percent Blue 32.27732493466359
     ------------------------------
Enhancement color: Aluminum_04.TIF   Value: 2.0
   Enhance image: Aluminum_04.TIF   Value: 2.0
   Save enhanced file : Enh_Aluminum_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Aluminum_04.TIF
   Red background for image: Enh_Aluminum_04.TIF
   Save masked image with red background: Mask_Enh_Aluminum_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Aluminum_04.TIF
    Main color from image: Mask_Enh_Aluminum_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1), ((249, 228, 223), 1)]
      Read color name: (233, 229, 223)
      Main Color file: Mask_Enh_Aluminum_04.TIF  RGB: [((233, 229, 223), 1)] (233, 229, 223)  Color name: gainsboro  Hex: #e9e5df
Aluminum  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 294.870824  Sum Ch 0: 99.847205  Sum Ch 1: 99.847205  Sum Ch 2: 95.176414
          Histog   : 94.37184   N.List elem: 768  Max: 239672 Idx Max: 11   Min: 0 Idx Min: 255
          Color    : gainsboro    RGB   : (233, 229, 223)    Hex color: #e9e5df   Dec Color: 15328735
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.8613375326682   Percentage G: 32.27732493466359   Percentage B: 32.27732493466359
File Start: 2019-06-01 18:35:22.570715  Finish: 2019-06-01 18:35:39.289886   File Time: 0:00:16.719171
Brass_01.TIF
------------------------------------------------------------------------
Inf.File: Brass_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_01.TIF
    RGB percent from image: Brass_01.TIF
     ------------------------------
     Percent Red 34.701500933436115
     Percent Green 34.701500933436115
     Percent Blue 30.596998133127773
     ------------------------------
Enhancement color: Brass_01.TIF   Value: 2.0
   Enhance image: Brass_01.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_01.TIF
   Red background for image: Enh_Brass_01.TIF
   Save masked image with red background: Mask_Enh_Brass_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_01.TIF
    Main color from image: Mask_Enh_Brass_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((242, 228, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_Brass_01.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 115.591735  Sum Ch 0: 40.112067  Sum Ch 1: 40.112067  Sum Ch 2: 35.367601
          Histog   : 94.37184   N.List elem: 768  Max: 310755 Idx Max: 11   Min: 0 Idx Min: 218
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.701500933436115   Percentage G: 30.596998133127773   Percentage B: 30.596998133127773
File Start: 2019-06-01 18:35:39.290382  Finish: 2019-06-01 18:35:50.156256   File Time: 0:00:10.865874
Brass_02.TIF
------------------------------------------------------------------------
Inf.File: Brass_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_02.TIF
    RGB percent from image: Brass_02.TIF
     ------------------------------
     Percent Red 34.674108757050625
     Percent Green 34.674108757050625
     Percent Blue 30.65178248589875
     ------------------------------
Enhancement color: Brass_02.TIF   Value: 2.0
   Enhance image: Brass_02.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_02.TIF
   Red background for image: Enh_Brass_02.TIF
   Save masked image with red background: Mask_Enh_Brass_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_02.TIF
    Main color from image: Mask_Enh_Brass_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((248, 228, 223), 1), ((234, 227, 223), 1), ((228, 227, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Brass_02.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 115.114431  Sum Ch 0: 39.914903  Sum Ch 1: 39.914903  Sum Ch 2: 35.284625
          Histog   : 94.37184   N.List elem: 768  Max: 303759 Idx Max: 11   Min: 0 Idx Min: 252
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.674108757050625   Percentage G: 30.65178248589875   Percentage B: 30.65178248589875
File Start: 2019-06-01 18:35:50.156256  Finish: 2019-06-01 18:36:00.921842   File Time: 0:00:10.765586
Brass_04.TIF
------------------------------------------------------------------------
Inf.File: Brass_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_04.TIF
    RGB percent from image: Brass_04.TIF
     ------------------------------
     Percent Red 34.69477478971919
     Percent Green 34.69477478971919
     Percent Blue 30.61045042056161
     ------------------------------
Enhancement color: Brass_04.TIF   Value: 2.0
   Enhance image: Brass_04.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_04.TIF
   Red background for image: Enh_Brass_04.TIF
   Save masked image with red background: Mask_Enh_Brass_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_04.TIF
    Main color from image: Mask_Enh_Brass_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((255, 228, 223), 1), ((250, 227, 223), 1), ((236, 227, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Brass_04.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 112.513003  Sum Ch 0: 39.036133  Sum Ch 1: 39.036133  Sum Ch 2: 34.440737
          Histog   : 94.37184   N.List elem: 768  Max: 303654 Idx Max: 10   Min: 0 Idx Min: 254
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.69477478971919   Percentage G: 30.61045042056161   Percentage B: 30.61045042056161
File Start: 2019-06-01 18:36:00.922336  Finish: 2019-06-01 18:36:10.014328   File Time: 0:00:09.091992
Brass_05.TIF
------------------------------------------------------------------------
Inf.File: Brass_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_05.TIF
    RGB percent from image: Brass_05.TIF
     ------------------------------
     Percent Red 34.71149162712858
     Percent Green 34.71149162712858
     Percent Blue 30.577016745742835
     ------------------------------
Enhancement color: Brass_05.TIF   Value: 2.0
   Enhance image: Brass_05.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_05.TIF
   Red background for image: Enh_Brass_05.TIF
   Save masked image with red background: Mask_Enh_Brass_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_05.TIF
    Main color from image: Mask_Enh_Brass_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((232, 229, 223), 1), ((230, 229, 223), 1), ((242, 228, 223), 1), ((252, 227, 223), 1)]
      Read color name: (232, 229, 223)
      Main Color file: Mask_Enh_Brass_05.TIF  RGB: [((232, 229, 223), 1)] (232, 229, 223)  Color name: gainsboro  Hex: #e8e5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 115.000094  Sum Ch 0: 39.918248  Sum Ch 1: 39.918248  Sum Ch 2: 35.163598
          Histog   : 94.37184   N.List elem: 768  Max: 304881 Idx Max: 11   Min: 0 Idx Min: 247
          Color    : gainsboro    RGB   : (232, 229, 223)    Hex color: #e8e5df   Dec Color: 15263199
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.71149162712858   Percentage G: 30.577016745742835   Percentage B: 30.577016745742835
File Start: 2019-06-01 18:36:10.014328  Finish: 2019-06-01 18:36:20.479397   File Time: 0:00:10.465069
Brass_06.TIF
------------------------------------------------------------------------
Inf.File: Brass_06.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_06.TIF
    RGB percent from image: Brass_06.TIF
     ------------------------------
     Percent Red 34.57756347141397
     Percent Green 34.57756347141397
     Percent Blue 30.844873057172066
     ------------------------------
Enhancement color: Brass_06.TIF   Value: 2.0
   Enhance image: Brass_06.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_06.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_06.TIF
   Red background for image: Enh_Brass_06.TIF
   Save masked image with red background: Mask_Enh_Brass_06.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_06.TIF
    Main color from image: Mask_Enh_Brass_06.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((249, 227, 223), 1), ((229, 227, 223), 1), ((227, 227, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_Brass_06.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 124.310095  Sum Ch 0: 42.983402  Sum Ch 1: 42.983402  Sum Ch 2: 38.343291
          Histog   : 94.37184   N.List elem: 768  Max: 317338 Idx Max: 11   Min: 0 Idx Min: 225
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.57756347141397   Percentage G: 30.844873057172066   Percentage B: 30.844873057172066
File Start: 2019-06-01 18:36:20.479894  Finish: 2019-06-01 18:36:29.228680   File Time: 0:00:08.748786
Brass_07.TIF
------------------------------------------------------------------------
Inf.File: Brass_07.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_07.TIF
    RGB percent from image: Brass_07.TIF
     ------------------------------
     Percent Red 34.63483992657567
     Percent Green 34.63483992657567
     Percent Blue 30.730320146848655
     ------------------------------
Enhancement color: Brass_07.TIF   Value: 2.0
   Enhance image: Brass_07.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_07.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_07.TIF
   Red background for image: Enh_Brass_07.TIF
   Save masked image with red background: Mask_Enh_Brass_07.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_07.TIF
    Main color from image: Mask_Enh_Brass_07.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((235, 229, 223), 1), ((229, 229, 223), 1), ((235, 227, 223), 1), ((227, 227, 223), 1)]
      Read color name: (235, 229, 223)
      Main Color file: Mask_Enh_Brass_07.TIF  RGB: [((235, 229, 223), 1)] (235, 229, 223)  Color name: gainsboro  Hex: #ebe5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 122.271733  Sum Ch 0: 42.348619  Sum Ch 1: 42.348619  Sum Ch 2: 37.574495
          Histog   : 94.37184   N.List elem: 768  Max: 318513 Idx Max: 12   Min: 0 Idx Min: 202
          Color    : gainsboro    RGB   : (235, 229, 223)    Hex color: #ebe5df   Dec Color: 15459807
          Extremes : ((1, 221), (1, 221), (0, 219)) Med Extremes: 110.5
          Percentage R: 34.63483992657567   Percentage G: 30.730320146848655   Percentage B: 30.730320146848655
File Start: 2019-06-01 18:36:29.228680  Finish: 2019-06-01 18:36:38.198430   File Time: 0:00:08.969750
Brass_08.TIF
------------------------------------------------------------------------
Inf.File: Brass_08.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_08.TIF
    RGB percent from image: Brass_08.TIF
     ------------------------------
     Percent Red 34.578000313845045
     Percent Green 34.578000313845045
     Percent Blue 30.843999372309906
     ------------------------------
Enhancement color: Brass_08.TIF   Value: 2.0
   Enhance image: Brass_08.TIF   Value: 2.0
   Save enhanced file : Enh_Brass_08.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Brass_08.TIF
   Red background for image: Enh_Brass_08.TIF
   Save masked image with red background: Mask_Enh_Brass_08.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Brass_08.TIF
    Main color from image: Mask_Enh_Brass_08.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((225, 225, 223), 1), ((255, 223, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_Brass_08.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
Brass  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 124.488185  Sum Ch 0: 43.045525  Sum Ch 1: 43.045525  Sum Ch 2: 38.397135
          Histog   : 94.37184   N.List elem: 768  Max: 320231 Idx Max: 11   Min: 0 Idx Min: 206
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 220), (1, 220), (0, 217)) Med Extremes: 109.83333333333333
          Percentage R: 34.578000313845045   Percentage G: 30.843999372309906   Percentage B: 30.843999372309906
File Start: 2019-06-01 18:36:38.198934  Finish: 2019-06-01 18:36:47.323715   File Time: 0:00:09.124781
CopperWire_01.TIF
------------------------------------------------------------------------
Inf.File: CopperWire_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_01.TIF
    RGB percent from image: CopperWire_01.TIF
     ------------------------------
     Percent Red 34.24620691364302
     Percent Green 34.24620691364302
     Percent Blue 31.507586172713953
     ------------------------------
Enhancement color: CopperWire_01.TIF   Value: 2.0
   Enhance image: CopperWire_01.TIF   Value: 2.0
   Save enhanced file : Enh_CopperWire_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_CopperWire_01.TIF
   Red background for image: Enh_CopperWire_01.TIF
   Save masked image with red background: Mask_Enh_CopperWire_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_CopperWire_01.TIF
    Main color from image: Mask_Enh_CopperWire_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((225, 225, 223), 1), ((223, 223, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_CopperWire_01.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 165.883977  Sum Ch 0: 56.80897  Sum Ch 1: 56.80897  Sum Ch 2: 52.266037
          Histog   : 94.37184   N.List elem: 768  Max: 327884 Idx Max: 10   Min: 0 Idx Min: 156
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 157), (1, 157), (0, 156)) Med Extremes: 78.66666666666667
          Percentage R: 34.24620691364302   Percentage G: 31.507586172713953   Percentage B: 31.507586172713953
File Start: 2019-06-01 18:36:47.323715  Finish: 2019-06-01 18:36:51.954436   File Time: 0:00:04.630721
CopperWire_02.TIF
------------------------------------------------------------------------
Inf.File: CopperWire_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_02.TIF
    RGB percent from image: CopperWire_02.TIF
     ------------------------------
     Percent Red 34.18076444353842
     Percent Green 34.18076444353842
     Percent Blue 31.63847111292316
     ------------------------------
Enhancement color: CopperWire_02.TIF   Value: 2.0
   Enhance image: CopperWire_02.TIF   Value: 2.0
   Save enhanced file : Enh_CopperWire_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_CopperWire_02.TIF
   Red background for image: Enh_CopperWire_02.TIF
   Save masked image with red background: Mask_Enh_CopperWire_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_CopperWire_02.TIF
    Main color from image: Mask_Enh_CopperWire_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((225, 225, 223), 1), ((223, 223, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_CopperWire_02.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 181.932704  Sum Ch 0: 62.185989  Sum Ch 1: 62.185989  Sum Ch 2: 57.560726
          Histog   : 94.37184   N.List elem: 768  Max: 303195 Idx Max: 9   Min: 0 Idx Min: 156
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 157), (1, 157), (0, 156)) Med Extremes: 78.66666666666667
          Percentage R: 34.18076444353842   Percentage G: 31.63847111292316   Percentage B: 31.63847111292316
File Start: 2019-06-01 18:36:51.954931  Finish: 2019-06-01 18:36:57.225071   File Time: 0:00:05.270140
CopperWire_03.TIF
------------------------------------------------------------------------
Inf.File: CopperWire_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_03.TIF
    RGB percent from image: CopperWire_03.TIF
     ------------------------------
     Percent Red 34.09631955546054
     Percent Green 34.09631955546054
     Percent Blue 31.807360889078918
     ------------------------------
Enhancement color: CopperWire_03.TIF   Value: 2.0
   Enhance image: CopperWire_03.TIF   Value: 2.0
   Save enhanced file : Enh_CopperWire_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_CopperWire_03.TIF
   Red background for image: Enh_CopperWire_03.TIF
   Save masked image with red background: Mask_Enh_CopperWire_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_CopperWire_03.TIF
    Main color from image: Mask_Enh_CopperWire_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((225, 225, 223), 1), ((223, 223, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_CopperWire_03.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 202.037768  Sum Ch 0: 68.887443  Sum Ch 1: 68.887443  Sum Ch 2: 64.262882
          Histog   : 94.37184   N.List elem: 768  Max: 295451 Idx Max: 9   Min: 0 Idx Min: 157
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 160), (1, 160), (0, 159)) Med Extremes: 80.16666666666667
          Percentage R: 34.09631955546054   Percentage G: 31.807360889078918   Percentage B: 31.807360889078918
File Start: 2019-06-01 18:36:57.225567  Finish: 2019-06-01 18:37:02.698830   File Time: 0:00:05.473263
CopperWire_04.TIF
------------------------------------------------------------------------
Inf.File: CopperWire_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_04.TIF
    RGB percent from image: CopperWire_04.TIF
     ------------------------------
     Percent Red 33.62690185419207
     Percent Green 33.62690185419207
     Percent Blue 32.74619629161587
     ------------------------------
Enhancement color: CopperWire_04.TIF   Value: 2.0
   Enhance image: CopperWire_04.TIF   Value: 2.0
   Save enhanced file : Enh_CopperWire_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_CopperWire_04.TIF
   Red background for image: Enh_CopperWire_04.TIF
   Save masked image with red background: Mask_Enh_CopperWire_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_CopperWire_04.TIF
    Main color from image: Mask_Enh_CopperWire_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((169, 165, 159), 1), ((166, 165, 159), 1), ((172, 164, 159), 1), ((166, 164, 159), 1)]
      Read color name: (169, 165, 159)
      Main Color file: Mask_Enh_CopperWire_04.TIF  RGB: [((169, 165, 159), 1)] (169, 165, 159)  Color name: darkgray  Hex: #a9a59f
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 531.395758  Sum Ch 0: 178.69193  Sum Ch 1: 178.69193  Sum Ch 2: 174.011898
          Histog   : 94.37184   N.List elem: 768  Max: 92231 Idx Max: 9   Min: 0 Idx Min: 131
          Color    : darkgray    RGB   : (169, 165, 159)    Hex color: #a9a59f   Dec Color: 11117983
          Extremes : ((1, 145), (1, 145), (0, 143)) Med Extremes: 72.5
          Percentage R: 33.62690185419207   Percentage G: 32.74619629161587   Percentage B: 32.74619629161587
File Start: 2019-06-01 18:37:02.698830  Finish: 2019-06-01 18:37:15.618268   File Time: 0:00:12.919438
CopperWire_05.TIF
------------------------------------------------------------------------
Inf.File: CopperWire_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_05.TIF
    RGB percent from image: CopperWire_05.TIF
     ------------------------------
     Percent Red 33.6250288890892
     Percent Green 33.6250288890892
     Percent Blue 32.749942221821605
     ------------------------------
Enhancement color: CopperWire_05.TIF   Value: 2.0
   Enhance image: CopperWire_05.TIF   Value: 2.0
   Save enhanced file : Enh_CopperWire_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_CopperWire_05.TIF
   Red background for image: Enh_CopperWire_05.TIF
   Save masked image with red background: Mask_Enh_CopperWire_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_CopperWire_05.TIF
    Main color from image: Mask_Enh_CopperWire_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((169, 165, 159), 1), ((166, 165, 159), 1), ((172, 164, 159), 1), ((166, 164, 159), 1)]
      Read color name: (169, 165, 159)
      Main Color file: Mask_Enh_CopperWire_05.TIF  RGB: [((169, 165, 159), 1)] (169, 165, 159)  Color name: darkgray  Hex: #a9a59f
CopperWire  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 535.292923  Sum Ch 0: 179.9924  Sum Ch 1: 179.9924  Sum Ch 2: 175.308123
          Histog   : 94.37184   N.List elem: 768  Max: 85458 Idx Max: 9   Min: 0 Idx Min: 131
          Color    : darkgray    RGB   : (169, 165, 159)    Hex color: #a9a59f   Dec Color: 11117983
          Extremes : ((1, 151), (1, 151), (0, 148)) Med Extremes: 75.33333333333333
          Percentage R: 33.6250288890892   Percentage G: 32.749942221821605   Percentage B: 32.749942221821605
File Start: 2019-06-01 18:37:15.620249  Finish: 2019-06-01 18:37:29.856958   File Time: 0:00:14.236709
Copper_01.TIF
------------------------------------------------------------------------
Inf.File: Copper_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_01.TIF
    RGB percent from image: Copper_01.TIF
     ------------------------------
     Percent Red 34.36707022525294
     Percent Green 34.36707022525294
     Percent Blue 31.265859549494124
     ------------------------------
Enhancement color: Copper_01.TIF   Value: 2.0
   Enhance image: Copper_01.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_01.TIF
   Red background for image: Enh_Copper_01.TIF
   Save masked image with red background: Mask_Enh_Copper_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_01.TIF
    Main color from image: Mask_Enh_Copper_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((227, 225, 223), 1), ((225, 225, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_Copper_01.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 147.616898  Sum Ch 0: 50.731603  Sum Ch 1: 50.731603  Sum Ch 2: 46.153692
          Histog   : 94.37184   N.List elem: 768  Max: 318670 Idx Max: 9   Min: 0 Idx Min: 153
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 169), (1, 169), (0, 169)) Med Extremes: 84.83333333333333
          Percentage R: 34.36707022525294   Percentage G: 31.265859549494124   Percentage B: 31.265859549494124
File Start: 2019-06-01 18:37:29.856958  Finish: 2019-06-01 18:37:39.414741   File Time: 0:00:09.557783
Copper_02.TIF
------------------------------------------------------------------------
Inf.File: Copper_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_02.TIF
    RGB percent from image: Copper_02.TIF
     ------------------------------
     Percent Red 34.458785430151
     Percent Green 34.458785430151
     Percent Blue 31.082429139698004
     ------------------------------
Enhancement color: Copper_02.TIF   Value: 2.0
   Enhance image: Copper_02.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_02.TIF
   Red background for image: Enh_Copper_02.TIF
   Save masked image with red background: Mask_Enh_Copper_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_02.TIF
    Main color from image: Mask_Enh_Copper_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((231, 229, 223), 1), ((229, 229, 223), 1), ((229, 227, 223), 1), ((227, 227, 223), 1)]
      Read color name: (231, 229, 223)
      Main Color file: Mask_Enh_Copper_02.TIF  RGB: [((231, 229, 223), 1)] (231, 229, 223)  Color name: gainsboro  Hex: #e7e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 142.227555  Sum Ch 0: 49.009888  Sum Ch 1: 49.009888  Sum Ch 2: 44.207779
          Histog   : 94.37184   N.List elem: 768  Max: 321043 Idx Max: 10   Min: 0 Idx Min: 160
          Color    : gainsboro    RGB   : (231, 229, 223)    Hex color: #e7e5df   Dec Color: 15197663
          Extremes : ((1, 179), (1, 179), (0, 179)) Med Extremes: 89.83333333333333
          Percentage R: 34.458785430151   Percentage G: 31.082429139698004   Percentage B: 31.082429139698004
File Start: 2019-06-01 18:37:39.415236  Finish: 2019-06-01 18:37:49.562476   File Time: 0:00:10.147240
Copper_03.TIF
------------------------------------------------------------------------
Inf.File: Copper_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_03.TIF
    RGB percent from image: Copper_03.TIF
     ------------------------------
     Percent Red 34.555104993905275
     Percent Green 34.555104993905275
     Percent Blue 30.88979001218945
     ------------------------------
Enhancement color: Copper_03.TIF   Value: 2.0
   Enhance image: Copper_03.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_03.TIF
   Red background for image: Enh_Copper_03.TIF
   Save masked image with red background: Mask_Enh_Copper_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_03.TIF
    Main color from image: Mask_Enh_Copper_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((228, 227, 223), 1), ((250, 226, 223), 1), ((228, 225, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Copper_03.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 130.53091  Sum Ch 0: 45.105093  Sum Ch 1: 45.105093  Sum Ch 2: 40.320724
          Histog   : 94.37184   N.List elem: 768  Max: 309376 Idx Max: 11   Min: 0 Idx Min: 162
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 168), (1, 168), (0, 166)) Med Extremes: 84.0
          Percentage R: 34.555104993905275   Percentage G: 30.88979001218945   Percentage B: 30.88979001218945
File Start: 2019-06-01 18:37:49.562972  Finish: 2019-06-01 18:37:58.319102   File Time: 0:00:08.756130
Copper_04.TIF
------------------------------------------------------------------------
Inf.File: Copper_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_04.TIF
    RGB percent from image: Copper_04.TIF
     ------------------------------
     Percent Red 34.31395400967227
     Percent Green 34.31395400967227
     Percent Blue 31.372091980655455
     ------------------------------
Enhancement color: Copper_04.TIF   Value: 2.0
   Enhance image: Copper_04.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_04.TIF
   Red background for image: Enh_Copper_04.TIF
   Save masked image with red background: Mask_Enh_Copper_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_04.TIF
    Main color from image: Mask_Enh_Copper_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((229, 229, 223), 1), ((227, 227, 223), 1), ((225, 225, 223), 1), ((223, 223, 223), 1)]
      Read color name: (229, 229, 223)
      Main Color file: Mask_Enh_Copper_04.TIF  RGB: [((229, 229, 223), 1)] (229, 229, 223)  Color name: gainsboro  Hex: #e5e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 163.031541  Sum Ch 0: 55.942568  Sum Ch 1: 55.942568  Sum Ch 2: 51.146405
          Histog   : 94.37184   N.List elem: 768  Max: 322371 Idx Max: 10   Min: 0 Idx Min: 155
          Color    : gainsboro    RGB   : (229, 229, 223)    Hex color: #e5e5df   Dec Color: 15066591
          Extremes : ((1, 156), (1, 156), (0, 155)) Med Extremes: 78.16666666666667
          Percentage R: 34.31395400967227   Percentage G: 31.372091980655455   Percentage B: 31.372091980655455
File Start: 2019-06-01 18:37:58.319102  Finish: 2019-06-01 18:38:05.757227   File Time: 0:00:07.438125
Copper_05.TIF
------------------------------------------------------------------------
Inf.File: Copper_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_05.TIF
    RGB percent from image: Copper_05.TIF
     ------------------------------
     Percent Red 34.358227085310126
     Percent Green 34.358227085310126
     Percent Blue 31.283545829379744
     ------------------------------
Enhancement color: Copper_05.TIF   Value: 2.0
   Enhance image: Copper_05.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_05.TIF
   Red background for image: Enh_Copper_05.TIF
   Save masked image with red background: Mask_Enh_Copper_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_05.TIF
    Main color from image: Mask_Enh_Copper_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((228, 227, 223), 1), ((226, 225, 223), 1), ((224, 223, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Copper_05.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 156.115532  Sum Ch 0: 53.638529  Sum Ch 1: 53.638529  Sum Ch 2: 48.838474
          Histog   : 94.37184   N.List elem: 768  Max: 329941 Idx Max: 10   Min: 0 Idx Min: 155
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 156), (1, 156), (0, 155)) Med Extremes: 78.16666666666667
          Percentage R: 34.358227085310126   Percentage G: 31.283545829379744   Percentage B: 31.283545829379744
File Start: 2019-06-01 18:38:05.757227  Finish: 2019-06-01 18:38:13.870137   File Time: 0:00:08.112910
Copper_06.TIF
------------------------------------------------------------------------
Inf.File: Copper_06.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_06.TIF
    RGB percent from image: Copper_06.TIF
     ------------------------------
     Percent Red 34.49952240739727
     Percent Green 34.49952240739727
     Percent Blue 31.00095518520546
     ------------------------------
Enhancement color: Copper_06.TIF   Value: 2.0
   Enhance image: Copper_06.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_06.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_06.TIF
   Red background for image: Enh_Copper_06.TIF
   Save masked image with red background: Mask_Enh_Copper_06.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_06.TIF
    Main color from image: Mask_Enh_Copper_06.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((228, 227, 223), 1), ((226, 225, 223), 1), ((224, 223, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Copper_06.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 137.346139  Sum Ch 0: 47.383762  Sum Ch 1: 47.383762  Sum Ch 2: 42.578615
          Histog   : 94.37184   N.List elem: 768  Max: 340247 Idx Max: 10   Min: 0 Idx Min: 153
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 154), (1, 154), (0, 153)) Med Extremes: 77.16666666666667
          Percentage R: 34.49952240739727   Percentage G: 31.00095518520546   Percentage B: 31.00095518520546
File Start: 2019-06-01 18:38:13.870137  Finish: 2019-06-01 18:38:22.926236   File Time: 0:00:09.056099
Copper_07.TIF
------------------------------------------------------------------------
Inf.File: Copper_07.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_07.TIF
    RGB percent from image: Copper_07.TIF
     ------------------------------
     Percent Red 34.65537597413545
     Percent Green 34.65537597413545
     Percent Blue 30.689248051729106
     ------------------------------
Enhancement color: Copper_07.TIF   Value: 2.0
   Enhance image: Copper_07.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_07.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_07.TIF
   Red background for image: Enh_Copper_07.TIF
   Save masked image with red background: Mask_Enh_Copper_07.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_07.TIF
    Main color from image: Mask_Enh_Copper_07.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((198, 197, 191), 1), ((239, 195, 191), 1), ((194, 191, 191), 1), ((166, 165, 159), 1)]
      Read color name: (198, 197, 191)
      Main Color file: Mask_Enh_Copper_07.TIF  RGB: [((198, 197, 191), 1)] (198, 197, 191)  Color name: silver  Hex: #c6c5bf
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 119.949182  Sum Ch 0: 41.56884  Sum Ch 1: 41.56884  Sum Ch 2: 36.811502
          Histog   : 94.37184   N.List elem: 768  Max: 303151 Idx Max: 11   Min: 0 Idx Min: 131
          Color    : silver    RGB   : (198, 197, 191)    Hex color: #c6c5bf   Dec Color: 13026751
          Extremes : ((1, 171), (1, 171), (0, 168)) Med Extremes: 85.33333333333333
          Percentage R: 34.65537597413545   Percentage G: 30.689248051729106   Percentage B: 30.689248051729106
File Start: 2019-06-01 18:38:22.926732  Finish: 2019-06-01 18:38:33.027958   File Time: 0:00:10.101226
Copper_08.TIF
------------------------------------------------------------------------
Inf.File: Copper_08.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_08.TIF
    RGB percent from image: Copper_08.TIF
     ------------------------------
     Percent Red 34.659485305146184
     Percent Green 34.659485305146184
     Percent Blue 30.681029389707636
     ------------------------------
Enhancement color: Copper_08.TIF   Value: 2.0
   Enhance image: Copper_08.TIF   Value: 2.0
   Save enhanced file : Enh_Copper_08.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Copper_08.TIF
   Red background for image: Enh_Copper_08.TIF
   Save masked image with red background: Mask_Enh_Copper_08.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Copper_08.TIF
    Main color from image: Mask_Enh_Copper_08.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((228, 227, 223), 1), ((255, 223, 223), 1), ((224, 223, 223), 1), ((198, 197, 191), 1)]
      Read color name: (228, 227, 223)
      Main Color file: Mask_Enh_Copper_08.TIF  RGB: [((228, 227, 223), 1)] (228, 227, 223)  Color name: gainsboro  Hex: #e4e3df
Copper  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 119.562843  Sum Ch 0: 41.439866  Sum Ch 1: 41.439866  Sum Ch 2: 36.683111
          Histog   : 94.37184   N.List elem: 768  Max: 305393 Idx Max: 9   Min: 0 Idx Min: 131
          Color    : gainsboro    RGB   : (228, 227, 223)    Hex color: #e4e3df   Dec Color: 15000543
          Extremes : ((1, 183), (1, 183), (0, 182)) Med Extremes: 91.66666666666667
          Percentage R: 34.659485305146184   Percentage G: 30.681029389707636   Percentage B: 30.681029389707636
File Start: 2019-06-01 18:38:33.027958  Finish: 2019-06-01 18:38:42.135051   File Time: 0:00:09.107093
Enh_Aluminum_01.TIF
------------------------------------------------------------------------
Inf.File: Enh_Aluminum_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Enh_Aluminum_01.TIF
    RGB percent from image: Enh_Aluminum_01.TIF
     ------------------------------
     Percent Red 33.7429237876734
     Percent Green 33.7429237876734
     Percent Blue 32.51415242465321
     ------------------------------
Enhancement color: Enh_Aluminum_01.TIF   Value: 2.0
   Enhance image: Enh_Aluminum_01.TIF   Value: 2.0
   Save enhanced file : Enh_Enh_Aluminum_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Enh_Aluminum_01.TIF
   Red background for image: Enh_Enh_Aluminum_01.TIF
   Save masked image with red background: Mask_Enh_Enh_Aluminum_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Enh_Aluminum_01.TIF
    Main color from image: Mask_Enh_Enh_Aluminum_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 235, 223), 1), ((243, 235, 223), 1), ((239, 235, 223), 1), ((235, 235, 223), 1)]
      Read color name: (255, 235, 223)
      Main Color file: Mask_Enh_Enh_Aluminum_01.TIF  RGB: [((255, 235, 223), 1)] (255, 235, 223)  Color name: mistyrose  Hex: #ffebdf
Enh  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 270.569538  Sum Ch 0: 91.298073  Sum Ch 1: 91.298073  Sum Ch 2: 87.973392
          Histog   : 94.37184   N.List elem: 768  Max: 2209745 Idx Max: 0   Min: 0 Idx Min: 1
          Color    : mistyrose    RGB   : (255, 235, 223)    Hex color: #ffebdf   Dec Color: 16772063
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 33.7429237876734   Percentage G: 32.51415242465321   Percentage B: 32.51415242465321
File Start: 2019-06-01 18:38:42.135051  Finish: 2019-06-01 18:38:52.332315   File Time: 0:00:10.197264
Enh_Aluminum_02.TIF
------------------------------------------------------------------------
Inf.File: Enh_Aluminum_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Enh_Aluminum_02.TIF
    RGB percent from image: Enh_Aluminum_02.TIF
     ------------------------------
     Percent Red 33.78060182471601
     Percent Green 33.78060182471601
     Percent Blue 32.438796350567976
     ------------------------------
Enhancement color: Enh_Aluminum_02.TIF   Value: 2.0
   Enhance image: Enh_Aluminum_02.TIF   Value: 2.0
   Save enhanced file : Enh_Enh_Aluminum_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Enh_Aluminum_02.TIF
   Red background for image: Enh_Enh_Aluminum_02.TIF
   Save masked image with red background: Mask_Enh_Enh_Aluminum_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Enh_Aluminum_02.TIF
    Main color from image: Mask_Enh_Enh_Aluminum_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((242, 235, 223), 1), ((238, 235, 223), 1), ((254, 234, 223), 1), ((250, 234, 223), 1)]
      Read color name: (242, 235, 223)
      Main Color file: Mask_Enh_Enh_Aluminum_02.TIF  RGB: [((242, 235, 223), 1)] (242, 235, 223)  Color name: beige  Hex: #f2ebdf
Enh  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 279.243234  Sum Ch 0: 94.330045  Sum Ch 1: 94.330045  Sum Ch 2: 90.583144
          Histog   : 94.37184   N.List elem: 768  Max: 1985670 Idx Max: 0   Min: 0 Idx Min: 1
          Color    : beige    RGB   : (242, 235, 223)    Hex color: #f2ebdf   Dec Color: 15920095
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 33.78060182471601   Percentage G: 32.438796350567976   Percentage B: 32.438796350567976
File Start: 2019-06-01 18:38:52.332315  Finish: 2019-06-01 18:39:02.838381   File Time: 0:00:10.506066
Enh_Aluminum_03.TIF
------------------------------------------------------------------------
Inf.File: Enh_Aluminum_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Enh_Aluminum_03.TIF
    RGB percent from image: Enh_Aluminum_03.TIF
     ------------------------------
     Percent Red 33.573867165410206
     Percent Green 33.573867165410206
     Percent Blue 32.852265669179594
     ------------------------------
Enhancement color: Enh_Aluminum_03.TIF   Value: 2.0
   Enhance image: Enh_Aluminum_03.TIF   Value: 2.0
   Save enhanced file : Enh_Enh_Aluminum_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Enh_Aluminum_03.TIF
   Red background for image: Enh_Enh_Aluminum_03.TIF
   Save masked image with red background: Mask_Enh_Enh_Aluminum_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Enh_Aluminum_03.TIF
    Main color from image: Mask_Enh_Enh_Aluminum_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((243, 235, 223), 1), ((239, 235, 223), 1), ((235, 235, 223), 1), ((255, 234, 223), 1)]
      Read color name: (243, 235, 223)
      Main Color file: Mask_Enh_Enh_Aluminum_03.TIF  RGB: [((243, 235, 223), 1)] (243, 235, 223)  Color name: beige  Hex: #f3ebdf
Enh  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 348.837137  Sum Ch 0: 117.118117  Sum Ch 1: 117.118117  Sum Ch 2: 114.600903
          Histog   : 94.37184   N.List elem: 768  Max: 2307529 Idx Max: 0   Min: 0 Idx Min: 1
          Color    : beige    RGB   : (243, 235, 223)    Hex color: #f3ebdf   Dec Color: 15985631
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 33.573867165410206   Percentage G: 32.852265669179594   Percentage B: 32.852265669179594
File Start: 2019-06-01 18:39:02.838876  Finish: 2019-06-01 18:39:12.869485   File Time: 0:00:10.030609
Enh_Aluminum_04.TIF
------------------------------------------------------------------------
Inf.File: Enh_Aluminum_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Enh_Aluminum_04.TIF
    RGB percent from image: Enh_Aluminum_04.TIF
     ------------------------------
     Percent Red 33.5718257571785
     Percent Green 33.5718257571785
     Percent Blue 32.856348485643
     ------------------------------
Enhancement color: Enh_Aluminum_04.TIF   Value: 2.0
   Enhance image: Enh_Aluminum_04.TIF   Value: 2.0
   Save enhanced file : Enh_Enh_Aluminum_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Enh_Aluminum_04.TIF
   Red background for image: Enh_Enh_Aluminum_04.TIF
   Save masked image with red background: Mask_Enh_Enh_Aluminum_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Enh_Aluminum_04.TIF
    Main color from image: Mask_Enh_Enh_Aluminum_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((239, 235, 223), 1), ((235, 235, 223), 1), ((255, 234, 223), 1), ((251, 234, 223), 1)]
      Read color name: (239, 235, 223)
      Main Color file: Mask_Enh_Enh_Aluminum_04.TIF  RGB: [((239, 235, 223), 1)] (239, 235, 223)  Color name: beige  Hex: #efebdf
Enh  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 366.798514  Sum Ch 0: 123.140958  Sum Ch 1: 123.140958  Sum Ch 2: 120.516598
          Histog   : 94.37184   N.List elem: 768  Max: 2225598 Idx Max: 0   Min: 0 Idx Min: 2
          Color    : beige    RGB   : (239, 235, 223)    Hex color: #efebdf   Dec Color: 15723487
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 33.5718257571785   Percentage G: 32.856348485643   Percentage B: 32.856348485643
File Start: 2019-06-01 18:39:12.869982  Finish: 2019-06-01 18:39:24.933423   File Time: 0:00:12.063441
Enh_Brass_01.TIF
------------------------------------------------------------------------
Inf.File: Enh_Brass_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Enh_Brass_01.TIF
    RGB percent from image: Enh_Brass_01.TIF
     ------------------------------
     Percent Red 35.4333735885169
     Percent Green 35.4333735885169
     Percent Blue 29.133252822966192
     ------------------------------
Enhancement color: Enh_Brass_01.TIF   Value: 2.0
   Enhance image: Enh_Brass_01.TIF   Value: 2.0
   Save enhanced file : Enh_Enh_Brass_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Enh_Brass_01.TIF
   Red background for image: Enh_Enh_Brass_01.TIF
   Save masked image with red background: Mask_Enh_Enh_Brass_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Enh_Brass_01.TIF
    Main color from image: Mask_Enh_Enh_Brass_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((243, 235, 223), 1), ((239, 235, 223), 1), ((235, 235, 223), 1), ((247, 231, 223), 1)]
      Read color name: (243, 235, 223)
      Main Color file: Mask_Enh_Enh_Brass_01.TIF  RGB: [((243, 235, 223), 1)] (243, 235, 223)  Color name: beige  Hex: #f3ebdf
Enh  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 122.4687  Sum Ch 0: 43.394792  Sum Ch 1: 43.394792  Sum Ch 2: 35.679116
          Histog   : 94.37184   N.List elem: 768  Max: 715308 Idx Max: 0   Min: 0 Idx Min: 1
          Color    : beige    RGB   : (243, 235, 223)    Hex color: #f3ebdf   Dec Color: 15985631
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 35.4333735885169   Percentage G: 29.133252822966192   Percentage B: 29.133252822966192
File Start: 2019-06-01 18:39:24.933920  Finish: 2019-06-01 18:39:32.269760   File Time: 0:00:07.335840
Iron_01.TIF
------------------------------------------------------------------------
Inf.File: Iron_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_01.TIF
    RGB percent from image: Iron_01.TIF
     ------------------------------
     Percent Red 33.900344832194875
     Percent Green 33.900344832194875
     Percent Blue 32.19931033561026
     ------------------------------
Enhancement color: Iron_01.TIF   Value: 2.0
   Enhance image: Iron_01.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_01.TIF
   Red background for image: Enh_Iron_01.TIF
   Save masked image with red background: Mask_Enh_Iron_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_01.TIF
    Main color from image: Mask_Enh_Iron_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((228, 227, 223), 1), ((226, 225, 223), 1), ((224, 223, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Iron_01.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 264.943069  Sum Ch 0: 89.816614  Sum Ch 1: 89.816614  Sum Ch 2: 85.309841
          Histog   : 94.37184   N.List elem: 768  Max: 233823 Idx Max: 10   Min: 0 Idx Min: 157
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 159), (1, 159), (0, 157)) Med Extremes: 79.5
          Percentage R: 33.900344832194875   Percentage G: 32.19931033561026   Percentage B: 32.19931033561026
File Start: 2019-06-01 18:39:32.270256  Finish: 2019-06-01 18:39:46.436693   File Time: 0:00:14.166437
Iron_02.TIF
------------------------------------------------------------------------
Inf.File: Iron_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_02.TIF
    RGB percent from image: Iron_02.TIF
     ------------------------------
     Percent Red 33.997855728481355
     Percent Green 33.997855728481355
     Percent Blue 32.004288543037305
     ------------------------------
Enhancement color: Iron_02.TIF   Value: 2.0
   Enhance image: Iron_02.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_02.TIF
   Red background for image: Enh_Iron_02.TIF
   Save masked image with red background: Mask_Enh_Iron_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_02.TIF
    Main color from image: Mask_Enh_Iron_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((228, 227, 223), 1), ((226, 225, 223), 1), ((226, 223, 223), 1), ((224, 223, 223), 1)]
      Read color name: (228, 227, 223)
      Main Color file: Mask_Enh_Iron_02.TIF  RGB: [((228, 227, 223), 1)] (228, 227, 223)  Color name: gainsboro  Hex: #e4e3df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 225.696231  Sum Ch 0: 76.731879  Sum Ch 1: 76.731879  Sum Ch 2: 72.232473
          Histog   : 94.37184   N.List elem: 768  Max: 249211 Idx Max: 10   Min: 0 Idx Min: 159
          Color    : gainsboro    RGB   : (228, 227, 223)    Hex color: #e4e3df   Dec Color: 15000543
          Extremes : ((1, 159), (1, 159), (0, 159)) Med Extremes: 79.83333333333333
          Percentage R: 33.997855728481355   Percentage G: 32.004288543037305   Percentage B: 32.004288543037305
File Start: 2019-06-01 18:39:46.437190  Finish: 2019-06-01 18:40:01.468526   File Time: 0:00:15.031336
Iron_03.TIF
------------------------------------------------------------------------
Inf.File: Iron_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_03.TIF
    RGB percent from image: Iron_03.TIF
     ------------------------------
     Percent Red 33.8705774800811
     Percent Green 33.8705774800811
     Percent Blue 32.258845039837794
     ------------------------------
Enhancement color: Iron_03.TIF   Value: 2.0
   Enhance image: Iron_03.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_03.TIF
   Red background for image: Enh_Iron_03.TIF
   Save masked image with red background: Mask_Enh_Iron_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_03.TIF
    Main color from image: Mask_Enh_Iron_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((232, 229, 223), 1), ((230, 229, 223), 1), ((228, 227, 223), 1), ((226, 225, 223), 1)]
      Read color name: (232, 229, 223)
      Main Color file: Mask_Enh_Iron_03.TIF  RGB: [((232, 229, 223), 1)] (232, 229, 223)  Color name: gainsboro  Hex: #e8e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 305.481411  Sum Ch 0: 103.468318  Sum Ch 1: 103.468318  Sum Ch 2: 98.544775
          Histog   : 94.37184   N.List elem: 768  Max: 212646 Idx Max: 11   Min: 0 Idx Min: 158
          Color    : gainsboro    RGB   : (232, 229, 223)    Hex color: #e8e5df   Dec Color: 15263199
          Extremes : ((1, 163), (1, 163), (0, 162)) Med Extremes: 81.66666666666667
          Percentage R: 33.8705774800811   Percentage G: 32.258845039837794   Percentage B: 32.258845039837794
File Start: 2019-06-01 18:40:01.469022  Finish: 2019-06-01 18:40:14.554013   File Time: 0:00:13.084991
Iron_04.TIF
------------------------------------------------------------------------
Inf.File: Iron_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_04.TIF
    RGB percent from image: Iron_04.TIF
     ------------------------------
     Percent Red 33.87970283850167
     Percent Green 33.87970283850167
     Percent Blue 32.24059432299667
     ------------------------------
Enhancement color: Iron_04.TIF   Value: 2.0
   Enhance image: Iron_04.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_04.TIF
   Red background for image: Enh_Iron_04.TIF
   Save masked image with red background: Mask_Enh_Iron_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_04.TIF
    Main color from image: Mask_Enh_Iron_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((228, 227, 223), 1), ((230, 225, 223), 1), ((226, 225, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Iron_04.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 300.732499  Sum Ch 0: 101.887277  Sum Ch 1: 101.887277  Sum Ch 2: 96.957945
          Histog   : 94.37184   N.List elem: 768  Max: 216346 Idx Max: 11   Min: 0 Idx Min: 160
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 163), (1, 163), (0, 163)) Med Extremes: 81.83333333333333
          Percentage R: 33.87970283850167   Percentage G: 32.24059432299667   Percentage B: 32.24059432299667
File Start: 2019-06-01 18:40:14.554013  Finish: 2019-06-01 18:40:29.601162   File Time: 0:00:15.047149
Iron_05.TIF
------------------------------------------------------------------------
Inf.File: Iron_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_05.TIF
    RGB percent from image: Iron_05.TIF
     ------------------------------
     Percent Red 34.126003035317325
     Percent Green 34.126003035317325
     Percent Blue 31.747993929365347
     ------------------------------
Enhancement color: Iron_05.TIF   Value: 2.0
   Enhance image: Iron_05.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_05.TIF
   Red background for image: Enh_Iron_05.TIF
   Save masked image with red background: Mask_Enh_Iron_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_05.TIF
    Main color from image: Mask_Enh_Iron_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((249, 229, 223), 1), ((235, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (249, 229, 223)
      Main Color file: Mask_Enh_Iron_05.TIF  RGB: [((249, 229, 223), 1)] (249, 229, 223)  Color name: mistyrose  Hex: #f9e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 204.72983  Sum Ch 0: 69.866108  Sum Ch 1: 69.866108  Sum Ch 2: 64.997614
          Histog   : 94.37184   N.List elem: 768  Max: 247280 Idx Max: 9   Min: 0 Idx Min: 155
          Color    : mistyrose    RGB   : (249, 229, 223)    Hex color: #f9e5df   Dec Color: 16377311
          Extremes : ((1, 157), (1, 157), (0, 155)) Med Extremes: 78.5
          Percentage R: 34.126003035317325   Percentage G: 31.747993929365347   Percentage B: 31.747993929365347
File Start: 2019-06-01 18:40:29.601656  Finish: 2019-06-01 18:40:42.907851   File Time: 0:00:13.306195
Iron_06.TIF
------------------------------------------------------------------------
Inf.File: Iron_06.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_06.TIF
    RGB percent from image: Iron_06.TIF
     ------------------------------
     Percent Red 34.08740130252074
     Percent Green 34.08740130252074
     Percent Blue 31.825197394958522
     ------------------------------
Enhancement color: Iron_06.TIF   Value: 2.0
   Enhance image: Iron_06.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_06.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_06.TIF
   Red background for image: Enh_Iron_06.TIF
   Save masked image with red background: Mask_Enh_Iron_06.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_06.TIF
    Main color from image: Mask_Enh_Iron_06.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((230, 229, 223), 1), ((230, 227, 223), 1), ((228, 227, 223), 1), ((240, 226, 223), 1)]
      Read color name: (230, 229, 223)
      Main Color file: Mask_Enh_Iron_06.TIF  RGB: [((230, 229, 223), 1)] (230, 229, 223)  Color name: gainsboro  Hex: #e6e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 214.413784  Sum Ch 0: 73.088087  Sum Ch 1: 73.088087  Sum Ch 2: 68.23761
          Histog   : 94.37184   N.List elem: 768  Max: 230280 Idx Max: 8   Min: 0 Idx Min: 151
          Color    : gainsboro    RGB   : (230, 229, 223)    Hex color: #e6e5df   Dec Color: 15132127
          Extremes : ((1, 168), (1, 168), (0, 168)) Med Extremes: 84.33333333333333
          Percentage R: 34.08740130252074   Percentage G: 31.825197394958522   Percentage B: 31.825197394958522
File Start: 2019-06-01 18:40:42.907851  Finish: 2019-06-01 18:40:57.239866   File Time: 0:00:14.332015
Iron_07.TIF
------------------------------------------------------------------------
Inf.File: Iron_07.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_07.TIF
    RGB percent from image: Iron_07.TIF
     ------------------------------
     Percent Red 34.06419399284424
     Percent Green 34.06419399284424
     Percent Blue 31.871612014311523
     ------------------------------
Enhancement color: Iron_07.TIF   Value: 2.0
   Enhance image: Iron_07.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_07.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_07.TIF
   Red background for image: Enh_Iron_07.TIF
   Save masked image with red background: Mask_Enh_Iron_07.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_07.TIF
    Main color from image: Mask_Enh_Iron_07.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1), ((245, 228, 223), 1)]
      Read color name: (233, 229, 223)
      Main Color file: Mask_Enh_Iron_07.TIF  RGB: [((233, 229, 223), 1)] (233, 229, 223)  Color name: gainsboro  Hex: #e9e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 216.771872  Sum Ch 0: 73.841591  Sum Ch 1: 73.841591  Sum Ch 2: 69.08869
          Histog   : 94.37184   N.List elem: 768  Max: 231255 Idx Max: 10   Min: 0 Idx Min: 155
          Color    : gainsboro    RGB   : (233, 229, 223)    Hex color: #e9e5df   Dec Color: 15328735
          Extremes : ((1, 173), (1, 173), (0, 173)) Med Extremes: 86.83333333333333
          Percentage R: 34.06419399284424   Percentage G: 31.871612014311523   Percentage B: 31.871612014311523
File Start: 2019-06-01 18:40:57.240362  Finish: 2019-06-01 18:41:13.655958   File Time: 0:00:16.415596
Iron_08.TIF
------------------------------------------------------------------------
Inf.File: Iron_08.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_08.TIF
    RGB percent from image: Iron_08.TIF
     ------------------------------
     Percent Red 34.1138005411127
     Percent Green 34.1138005411127
     Percent Blue 31.772398917774584
     ------------------------------
Enhancement color: Iron_08.TIF   Value: 2.0
   Enhance image: Iron_08.TIF   Value: 2.0
   Save enhanced file : Enh_Iron_08.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_Iron_08.TIF
   Red background for image: Enh_Iron_08.TIF
   Save masked image with red background: Mask_Enh_Iron_08.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_Iron_08.TIF
    Main color from image: Mask_Enh_Iron_08.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((243, 229, 223), 1), ((235, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (243, 229, 223)
      Main Color file: Mask_Enh_Iron_08.TIF  RGB: [((243, 229, 223), 1)] (243, 229, 223)  Color name: mistyrose  Hex: #f3e5df
Iron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 203.316977  Sum Ch 0: 69.359148  Sum Ch 1: 69.359148  Sum Ch 2: 64.598681
          Histog   : 94.37184   N.List elem: 768  Max: 232920 Idx Max: 10   Min: 0 Idx Min: 154
          Color    : mistyrose    RGB   : (243, 229, 223)    Hex color: #f3e5df   Dec Color: 15984095
          Extremes : ((1, 233), (1, 233), (0, 230)) Med Extremes: 116.33333333333333
          Percentage R: 34.1138005411127   Percentage G: 31.772398917774584   Percentage B: 31.772398917774584
File Start: 2019-06-01 18:41:13.655958  Finish: 2019-06-01 18:41:31.141129   File Time: 0:00:17.485171
MASK_Enh_Aluminum_01.TIF
------------------------------------------------------------------------
Inf.File: MASK_Enh_Aluminum_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/MASK_Enh_Aluminum_01.TIF
    RGB percent from image: MASK_Enh_Aluminum_01.TIF
     ------------------------------
     Percent Red 84.26545228995984
     Percent Green 7.9412458026814905
     Percent Blue 7.79330190735867
     ------------------------------
Enhancement color: MASK_Enh_Aluminum_01.TIF   Value: 2.0
   Enhance image: MASK_Enh_Aluminum_01.TIF   Value: 2.0
   Save enhanced file : Enh_MASK_Enh_Aluminum_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_MASK_Enh_Aluminum_01.TIF
   Red background for image: Enh_MASK_Enh_Aluminum_01.TIF
   Save masked image with red background: Mask_Enh_MASK_Enh_Aluminum_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_MASK_Enh_Aluminum_01.TIF
    Main color from image: Mask_Enh_MASK_Enh_Aluminum_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 235, 223), 1), ((243, 235, 223), 1), ((241, 235, 223), 1), ((237, 235, 223), 1)]
      Read color name: (255, 235, 223)
      Main Color file: Mask_Enh_MASK_Enh_Aluminum_01.TIF  RGB: [((255, 235, 223), 1)] (255, 235, 223)  Color name: mistyrose  Hex: #ffebdf
MASK  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 889.02891  Sum Ch 0: 749.144232  Sum Ch 1: 70.599971  Sum Ch 2: 69.284707
          Histog   : 94.37184   N.List elem: 768  Max: 2654298 Idx Max: 0   Min: 21 Idx Min: 243
          Color    : mistyrose    RGB   : (255, 235, 223)    Hex color: #ffebdf   Dec Color: 16772063
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 84.26545228995984   Percentage G: 7.79330190735867   Percentage B: 7.79330190735867
File Start: 2019-06-01 18:41:31.141625  Finish: 2019-06-01 18:41:41.399952   File Time: 0:00:10.258327
MASK_Enh_Aluminum_02.TIF
------------------------------------------------------------------------
Inf.File: MASK_Enh_Aluminum_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/MASK_Enh_Aluminum_02.TIF
    RGB percent from image: MASK_Enh_Aluminum_02.TIF
     ------------------------------
     Percent Red 84.75136688587929
     Percent Green 7.695266112391577
     Percent Blue 7.553367001729125
     ------------------------------
Enhancement color: MASK_Enh_Aluminum_02.TIF   Value: 2.0
   Enhance image: MASK_Enh_Aluminum_02.TIF   Value: 2.0
   Save enhanced file : Enh_MASK_Enh_Aluminum_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_MASK_Enh_Aluminum_02.TIF
   Red background for image: Enh_MASK_Enh_Aluminum_02.TIF
   Save masked image with red background: Mask_Enh_MASK_Enh_Aluminum_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_MASK_Enh_Aluminum_02.TIF
    Main color from image: Mask_Enh_MASK_Enh_Aluminum_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((238, 235, 223), 1), ((255, 234, 223), 1), ((238, 234, 223), 1), ((255, 233, 223), 1)]
      Read color name: (238, 235, 223)
      Main Color file: Mask_Enh_MASK_Enh_Aluminum_02.TIF  RGB: [((238, 235, 223), 1)] (238, 235, 223)  Color name: beige  Hex: #eeebdf
MASK  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 886.216266  Sum Ch 0: 751.080399  Sum Ch 1: 68.1967  Sum Ch 2: 66.939167
          Histog   : 94.37184   N.List elem: 768  Max: 2646428 Idx Max: 0   Min: 12 Idx Min: 243
          Color    : beige    RGB   : (238, 235, 223)    Hex color: #eeebdf   Dec Color: 15657951
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 84.75136688587929   Percentage G: 7.553367001729125   Percentage B: 7.553367001729125
File Start: 2019-06-01 18:41:41.399952  Finish: 2019-06-01 18:41:52.528736   File Time: 0:00:11.128784
MASK_Enh_Aluminum_03.TIF
------------------------------------------------------------------------
Inf.File: MASK_Enh_Aluminum_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/MASK_Enh_Aluminum_03.TIF
    RGB percent from image: MASK_Enh_Aluminum_03.TIF
     ------------------------------
     Percent Red 81.51839467781946
     Percent Green 9.324209853458107
     Percent Blue 9.157395468722424
     ------------------------------
Enhancement color: MASK_Enh_Aluminum_03.TIF   Value: 2.0
   Enhance image: MASK_Enh_Aluminum_03.TIF   Value: 2.0
   Save enhanced file : Enh_MASK_Enh_Aluminum_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_MASK_Enh_Aluminum_03.TIF
   Red background for image: Enh_MASK_Enh_Aluminum_03.TIF
   Save masked image with red background: Mask_Enh_MASK_Enh_Aluminum_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_MASK_Enh_Aluminum_03.TIF
    Main color from image: Mask_Enh_MASK_Enh_Aluminum_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 235, 223), 1), ((241, 235, 223), 1), ((239, 235, 223), 1), ((235, 235, 223), 1)]
      Read color name: (255, 235, 223)
      Main Color file: Mask_Enh_MASK_Enh_Aluminum_03.TIF  RGB: [((255, 235, 223), 1)] (255, 235, 223)  Color name: mistyrose  Hex: #ffebdf
MASK  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 908.627276  Sum Ch 0: 740.698369  Sum Ch 1: 84.722314  Sum Ch 2: 83.206593
          Histog   : 94.37184   N.List elem: 768  Max: 2547283 Idx Max: 0   Min: 58 Idx Min: 235
          Color    : mistyrose    RGB   : (255, 235, 223)    Hex color: #ffebdf   Dec Color: 16772063
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 81.51839467781946   Percentage G: 9.157395468722424   Percentage B: 9.157395468722424
File Start: 2019-06-01 18:41:52.528736  Finish: 2019-06-01 18:42:04.189954   File Time: 0:00:11.661218
MASK_Enh_Aluminum_04.TIF
------------------------------------------------------------------------
Inf.File: MASK_Enh_Aluminum_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/MASK_Enh_Aluminum_04.TIF
    RGB percent from image: MASK_Enh_Aluminum_04.TIF
     ------------------------------
     Percent Red 80.80042373508218
     Percent Green 9.689759296931316
     Percent Blue 9.509816967986518
     ------------------------------
Enhancement color: MASK_Enh_Aluminum_04.TIF   Value: 2.0
   Enhance image: MASK_Enh_Aluminum_04.TIF   Value: 2.0
   Save enhanced file : Enh_MASK_Enh_Aluminum_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_MASK_Enh_Aluminum_04.TIF
   Red background for image: Enh_MASK_Enh_Aluminum_04.TIF
   Save masked image with red background: Mask_Enh_MASK_Enh_Aluminum_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_MASK_Enh_Aluminum_04.TIF
    Main color from image: Mask_Enh_MASK_Enh_Aluminum_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 235, 223), 1), ((253, 235, 223), 1), ((243, 235, 223), 1), ((239, 235, 223), 1)]
      Read color name: (255, 235, 223)
      Main Color file: Mask_Enh_MASK_Enh_Aluminum_04.TIF  RGB: [((255, 235, 223), 1)] (255, 235, 223)  Color name: mistyrose  Hex: #ffebdf
MASK  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 892.191409  Sum Ch 0: 720.894439  Sum Ch 1: 86.4512  Sum Ch 2: 84.84577
          Histog   : 94.37184   N.List elem: 768  Max: 2492067 Idx Max: 0   Min: 24 Idx Min: 252
          Color    : mistyrose    RGB   : (255, 235, 223)    Hex color: #ffebdf   Dec Color: 16772063
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 80.80042373508218   Percentage G: 9.509816967986518   Percentage B: 9.509816967986518
File Start: 2019-06-01 18:42:04.189954  Finish: 2019-06-01 18:42:15.161475   File Time: 0:00:10.971521
MASK_Enh_Brass_01.TIF
------------------------------------------------------------------------
Inf.File: MASK_Enh_Brass_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/MASK_Enh_Brass_01.TIF
    RGB percent from image: MASK_Enh_Brass_01.TIF
     ------------------------------
     Percent Red 97.66194395305871
     Percent Green 1.1858285741113515
     Percent Blue 1.1522274728299384
     ------------------------------
Enhancement color: MASK_Enh_Brass_01.TIF   Value: 2.0
   Enhance image: MASK_Enh_Brass_01.TIF   Value: 2.0
   Save enhanced file : Enh_MASK_Enh_Brass_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_MASK_Enh_Brass_01.TIF
   Red background for image: Enh_MASK_Enh_Brass_01.TIF
   Save masked image with red background: Mask_Enh_MASK_Enh_Brass_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_MASK_Enh_Brass_01.TIF
    Main color from image: Mask_Enh_MASK_Enh_Brass_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((255, 235, 223), 1), ((239, 235, 223), 1), ((235, 235, 223), 1), ((255, 234, 223), 1)]
      Read color name: (255, 235, 223)
      Main Color file: Mask_Enh_MASK_Enh_Brass_01.TIF  RGB: [((255, 235, 223), 1)] (255, 235, 223)  Color name: mistyrose  Hex: #ffebdf
MASK  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 805.646213  Sum Ch 0: 786.809753  Sum Ch 1: 9.553583  Sum Ch 2: 9.282877
          Histog   : 94.37184   N.List elem: 768  Max: 3025475 Idx Max: 0   Min: 40 Idx Min: 245
          Color    : mistyrose    RGB   : (255, 235, 223)    Hex color: #ffebdf   Dec Color: 16772063
          Extremes : ((0, 255), (0, 255), (0, 255)) Med Extremes: 127.5
          Percentage R: 97.66194395305871   Percentage G: 1.1522274728299384   Percentage B: 1.1522274728299384
File Start: 2019-06-01 18:42:15.161970  Finish: 2019-06-01 18:42:24.345448   File Time: 0:00:09.183478
PaintedIron_01.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_01.TIF
    RGB percent from image: PaintedIron_01.TIF
     ------------------------------
     Percent Red 34.295364240934745
     Percent Green 34.295364240934745
     Percent Blue 31.409271518130517
     ------------------------------
Enhancement color: PaintedIron_01.TIF   Value: 2.0
   Enhance image: PaintedIron_01.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_01.TIF
   Red background for image: Enh_PaintedIron_01.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_01.TIF
    Main color from image: Mask_Enh_PaintedIron_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((246, 229, 223), 1), ((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1)]
      Read color name: (246, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_01.TIF  RGB: [((246, 229, 223), 1)] (246, 229, 223)  Color name: mistyrose  Hex: #f6e5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 155.320928  Sum Ch 0: 53.267878  Sum Ch 1: 53.267878  Sum Ch 2: 48.785172
          Histog   : 94.37184   N.List elem: 768  Max: 345478 Idx Max: 9   Min: 0 Idx Min: 149
          Color    : mistyrose    RGB   : (246, 229, 223)    Hex color: #f6e5df   Dec Color: 16180703
          Extremes : ((1, 154), (1, 154), (0, 153)) Med Extremes: 77.16666666666667
          Percentage R: 34.295364240934745   Percentage G: 31.409271518130517   Percentage B: 31.409271518130517
File Start: 2019-06-01 18:42:24.346937  Finish: 2019-06-01 18:42:55.657934   File Time: 0:00:31.310997
PaintedIron_02.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_02.TIF
    RGB percent from image: PaintedIron_02.TIF
     ------------------------------
     Percent Red 34.32501995011145
     Percent Green 34.32501995011145
     Percent Blue 31.349960099777107
     ------------------------------
Enhancement color: PaintedIron_02.TIF   Value: 2.0
   Enhance image: PaintedIron_02.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_02.TIF
   Red background for image: Enh_PaintedIron_02.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_02.TIF
    Main color from image: Mask_Enh_PaintedIron_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((242, 228, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_02.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 150.476603  Sum Ch 0: 51.651124  Sum Ch 1: 51.651124  Sum Ch 2: 47.174355
          Histog   : 94.37184   N.List elem: 768  Max: 353839 Idx Max: 8   Min: 0 Idx Min: 145
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 152), (1, 152), (0, 149)) Med Extremes: 75.83333333333333
          Percentage R: 34.32501995011145   Percentage G: 31.349960099777107   Percentage B: 31.349960099777107
File Start: 2019-06-01 18:42:55.658430  Finish: 2019-06-01 18:43:07.224159   File Time: 0:00:11.565729
PaintedIron_03.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_03.TIF
    RGB percent from image: PaintedIron_03.TIF
     ------------------------------
     Percent Red 34.490350632134536
     Percent Green 34.490350632134536
     Percent Blue 31.01929873573094
     ------------------------------
Enhancement color: PaintedIron_03.TIF   Value: 2.0
   Enhance image: PaintedIron_03.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_03.TIF
   Red background for image: Enh_PaintedIron_03.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_03.TIF
    Main color from image: Mask_Enh_PaintedIron_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((235, 229, 223), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (235, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_03.TIF  RGB: [((235, 229, 223), 1)] (235, 229, 223)  Color name: gainsboro  Hex: #ebe5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 142.346993  Sum Ch 0: 49.095977  Sum Ch 1: 49.095977  Sum Ch 2: 44.155039
          Histog   : 94.37184   N.List elem: 768  Max: 356636 Idx Max: 8   Min: 0 Idx Min: 142
          Color    : gainsboro    RGB   : (235, 229, 223)    Hex color: #ebe5df   Dec Color: 15459807
          Extremes : ((1, 147), (1, 147), (0, 145)) Med Extremes: 73.5
          Percentage R: 34.490350632134536   Percentage G: 31.01929873573094   Percentage B: 31.01929873573094
File Start: 2019-06-01 18:43:07.224159  Finish: 2019-06-01 18:43:18.046987   File Time: 0:00:10.822828
PaintedIron_04.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_04.TIF
    RGB percent from image: PaintedIron_04.TIF
     ------------------------------
     Percent Red 34.47729495360336
     Percent Green 34.47729495360336
     Percent Blue 31.045410092793276
     ------------------------------
Enhancement color: PaintedIron_04.TIF   Value: 2.0
   Enhance image: PaintedIron_04.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_04.TIF
   Red background for image: Enh_PaintedIron_04.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_04.TIF
    Main color from image: Mask_Enh_PaintedIron_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((235, 229, 223), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (235, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_04.TIF  RGB: [((235, 229, 223), 1)] (235, 229, 223)  Color name: gainsboro  Hex: #ebe5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 143.721838  Sum Ch 0: 49.551402  Sum Ch 1: 49.551402  Sum Ch 2: 44.619034
          Histog   : 94.37184   N.List elem: 768  Max: 371011 Idx Max: 7   Min: 0 Idx Min: 136
          Color    : gainsboro    RGB   : (235, 229, 223)    Hex color: #ebe5df   Dec Color: 15459807
          Extremes : ((1, 146), (1, 146), (0, 144)) Med Extremes: 73.0
          Percentage R: 34.47729495360336   Percentage G: 31.045410092793276   Percentage B: 31.045410092793276
File Start: 2019-06-01 18:43:18.047483  Finish: 2019-06-01 18:43:29.074225   File Time: 0:00:11.026742
PaintedIron_05.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_05.TIF
    RGB percent from image: PaintedIron_05.TIF
     ------------------------------
     Percent Red 34.281060833040534
     Percent Green 34.281060833040534
     Percent Blue 31.437878333918935
     ------------------------------
Enhancement color: PaintedIron_05.TIF   Value: 2.0
   Enhance image: PaintedIron_05.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_05.TIF
   Red background for image: Enh_PaintedIron_05.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_05.TIF
    Main color from image: Mask_Enh_PaintedIron_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((243, 229, 223), 1), ((235, 229, 223), 1), ((233, 229, 223), 1), ((231, 229, 223), 1)]
      Read color name: (243, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_05.TIF  RGB: [((243, 229, 223), 1)] (243, 229, 223)  Color name: mistyrose  Hex: #f3e5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 162.789726  Sum Ch 0: 55.806045  Sum Ch 1: 55.806045  Sum Ch 2: 51.177636
          Histog   : 94.37184   N.List elem: 768  Max: 352642 Idx Max: 8   Min: 0 Idx Min: 141
          Color    : mistyrose    RGB   : (243, 229, 223)    Hex color: #f3e5df   Dec Color: 15984095
          Extremes : ((1, 152), (1, 152), (0, 150)) Med Extremes: 76.0
          Percentage R: 34.281060833040534   Percentage G: 31.437878333918935   Percentage B: 31.437878333918935
File Start: 2019-06-01 18:43:29.074225  Finish: 2019-06-01 18:43:43.170544   File Time: 0:00:14.096319
PaintedIron_06.TIF
------------------------------------------------------------------------
Inf.File: PaintedIron_06.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_06.TIF
    RGB percent from image: PaintedIron_06.TIF
     ------------------------------
     Percent Red 34.4042619520464
     Percent Green 34.4042619520464
     Percent Blue 31.191476095907213
     ------------------------------
Enhancement color: PaintedIron_06.TIF   Value: 2.0
   Enhance image: PaintedIron_06.TIF   Value: 2.0
   Save enhanced file : Enh_PaintedIron_06.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_PaintedIron_06.TIF
   Red background for image: Enh_PaintedIron_06.TIF
   Save masked image with red background: Mask_Enh_PaintedIron_06.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_PaintedIron_06.TIF
    Main color from image: Mask_Enh_PaintedIron_06.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((243, 229, 223), 1), ((235, 229, 223), 1), ((233, 229, 223), 1), ((231, 229, 223), 1)]
      Read color name: (243, 229, 223)
      Main Color file: Mask_Enh_PaintedIron_06.TIF  RGB: [((243, 229, 223), 1)] (243, 229, 223)  Color name: mistyrose  Hex: #f3e5df
PaintedIron  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 148.26164  Sum Ch 0: 51.008323  Sum Ch 1: 51.008323  Sum Ch 2: 46.244994
          Histog   : 94.37184   N.List elem: 768  Max: 339650 Idx Max: 7   Min: 0 Idx Min: 143
          Color    : mistyrose    RGB   : (243, 229, 223)    Hex color: #f3e5df   Dec Color: 15984095
          Extremes : ((1, 153), (1, 153), (0, 150)) Med Extremes: 76.33333333333333
          Percentage R: 34.4042619520464   Percentage G: 31.191476095907213   Percentage B: 31.191476095907213
File Start: 2019-06-01 18:43:43.170544  Finish: 2019-06-01 18:43:57.796531   File Time: 0:00:14.625987
StainlessSteel_01.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_01.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_01.TIF
    RGB percent from image: StainlessSteel_01.TIF
     ------------------------------
     Percent Red 33.86898842021843
     Percent Green 33.86898842021843
     Percent Blue 32.26202315956314
     ------------------------------
Enhancement color: StainlessSteel_01.TIF   Value: 2.0
   Enhance image: StainlessSteel_01.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_01.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_01.TIF
   Red background for image: Enh_StainlessSteel_01.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_01.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_01.TIF
    Main color from image: Mask_Enh_StainlessSteel_01.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1), ((231, 228, 223), 1)]
      Read color name: (233, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_01.TIF  RGB: [((233, 229, 223), 1)] (233, 229, 223)  Color name: gainsboro  Hex: #e9e5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 280.133436  Sum Ch 0: 94.878361  Sum Ch 1: 94.878361  Sum Ch 2: 90.376714
          Histog   : 94.37184   N.List elem: 768  Max: 263983 Idx Max: 10   Min: 0 Idx Min: 255
          Color    : gainsboro    RGB   : (233, 229, 223)    Hex color: #e9e5df   Dec Color: 15328735
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.86898842021843   Percentage G: 32.26202315956314   Percentage B: 32.26202315956314
File Start: 2019-06-01 18:43:57.797027  Finish: 2019-06-01 18:44:10.495963   File Time: 0:00:12.698936
StainlessSteel_02.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_02.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_02.TIF
    RGB percent from image: StainlessSteel_02.TIF
     ------------------------------
     Percent Red 33.94310863375323
     Percent Green 33.94310863375323
     Percent Blue 32.11378273249354
     ------------------------------
Enhancement color: StainlessSteel_02.TIF   Value: 2.0
   Enhance image: StainlessSteel_02.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_02.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_02.TIF
   Red background for image: Enh_StainlessSteel_02.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_02.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_02.TIF
    Main color from image: Mask_Enh_StainlessSteel_02.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((244, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((234, 228, 223), 1)]
      Read color name: (244, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_02.TIF  RGB: [((244, 229, 223), 1)] (244, 229, 223)  Color name: mistyrose  Hex: #f4e5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 245.591395  Sum Ch 0: 83.361354  Sum Ch 1: 83.361354  Sum Ch 2: 78.868687
          Histog   : 94.37184   N.List elem: 768  Max: 284112 Idx Max: 10   Min: 0 Idx Min: 255
          Color    : mistyrose    RGB   : (244, 229, 223)    Hex color: #f4e5df   Dec Color: 16049631
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.94310863375323   Percentage G: 32.11378273249354   Percentage B: 32.11378273249354
File Start: 2019-06-01 18:44:10.495963  Finish: 2019-06-01 18:44:20.598810   File Time: 0:00:10.102847
StainlessSteel_03.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_03.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_03.TIF
    RGB percent from image: StainlessSteel_03.TIF
     ------------------------------
     Percent Red 34.12214160492534
     Percent Green 34.12214160492534
     Percent Blue 31.755716790149318
     ------------------------------
Enhancement color: StainlessSteel_03.TIF   Value: 2.0
   Enhance image: StainlessSteel_03.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_03.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_03.TIF
   Red background for image: Enh_StainlessSteel_03.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_03.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_03.TIF
    Main color from image: Mask_Enh_StainlessSteel_03.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((232, 229, 223), 1), ((230, 229, 223), 1), ((236, 227, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_03.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 208.979553  Sum Ch 0: 71.308299  Sum Ch 1: 71.308299  Sum Ch 2: 66.362955
          Histog   : 94.37184   N.List elem: 768  Max: 297745 Idx Max: 11   Min: 0 Idx Min: 255
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 34.12214160492534   Percentage G: 31.755716790149318   Percentage B: 31.755716790149318
File Start: 2019-06-01 18:44:20.598810  Finish: 2019-06-01 18:44:31.589449   File Time: 0:00:10.990639
StainlessSteel_04.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_04.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_04.TIF
    RGB percent from image: StainlessSteel_04.TIF
     ------------------------------
     Percent Red 33.99708308700248
     Percent Green 33.99708308700248
     Percent Blue 32.005833825995055
     ------------------------------
Enhancement color: StainlessSteel_04.TIF   Value: 2.0
   Enhance image: StainlessSteel_04.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_04.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_04.TIF
   Red background for image: Enh_StainlessSteel_04.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_04.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_04.TIF
    Main color from image: Mask_Enh_StainlessSteel_04.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((234, 229, 223), 1), ((230, 229, 223), 1), ((234, 227, 223), 1), ((232, 227, 223), 1)]
      Read color name: (234, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_04.TIF  RGB: [((234, 229, 223), 1)] (234, 229, 223)  Color name: gainsboro  Hex: #eae5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 247.456815  Sum Ch 0: 84.128099  Sum Ch 1: 84.128099  Sum Ch 2: 79.200617
          Histog   : 94.37184   N.List elem: 768  Max: 296137 Idx Max: 12   Min: 0 Idx Min: 240
          Color    : gainsboro    RGB   : (234, 229, 223)    Hex color: #eae5df   Dec Color: 15394271
          Extremes : ((1, 255), (1, 255), (0, 255)) Med Extremes: 127.83333333333333
          Percentage R: 33.99708308700248   Percentage G: 32.005833825995055   Percentage B: 32.005833825995055
File Start: 2019-06-01 18:44:31.589449  Finish: 2019-06-01 18:44:41.545659   File Time: 0:00:09.956210
StainlessSteel_05.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_05.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_05.TIF
    RGB percent from image: StainlessSteel_05.TIF
     ------------------------------
     Percent Red 34.097956306225704
     Percent Green 34.097956306225704
     Percent Blue 31.804087387548595
     ------------------------------
Enhancement color: StainlessSteel_05.TIF   Value: 2.0
   Enhance image: StainlessSteel_05.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_05.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_05.TIF
   Red background for image: Enh_StainlessSteel_05.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_05.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_05.TIF
    Main color from image: Mask_Enh_StainlessSteel_05.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((235, 229, 223), 1), ((233, 229, 223), 1), ((231, 229, 223), 1), ((229, 229, 223), 1)]
      Read color name: (235, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_05.TIF  RGB: [((235, 229, 223), 1)] (235, 229, 223)  Color name: gainsboro  Hex: #ebe5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 214.909839  Sum Ch 0: 73.279863  Sum Ch 1: 73.279863  Sum Ch 2: 68.350113
          Histog   : 94.37184   N.List elem: 768  Max: 304852 Idx Max: 11   Min: 0 Idx Min: 254
          Color    : gainsboro    RGB   : (235, 229, 223)    Hex color: #ebe5df   Dec Color: 15459807
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.097956306225704   Percentage G: 31.804087387548595   Percentage B: 31.804087387548595
File Start: 2019-06-01 18:44:41.546156  Finish: 2019-06-01 18:44:50.911619   File Time: 0:00:09.365463
StainlessSteel_06.TIF
------------------------------------------------------------------------
Inf.File: StainlessSteel_06.TIF
Get Channel n:  0
Get Channel n:  1
Get Channel n:  2
Histogram analisys: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_06.TIF
    RGB percent from image: StainlessSteel_06.TIF
     ------------------------------
     Percent Red 34.14182248475463
     Percent Green 34.14182248475463
     Percent Blue 31.71635503049075
     ------------------------------
Enhancement color: StainlessSteel_06.TIF   Value: 2.0
   Enhance image: StainlessSteel_06.TIF   Value: 2.0
   Save enhanced file : Enh_StainlessSteel_06.TIF
Red background: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Enh_StainlessSteel_06.TIF
   Red background for image: Enh_StainlessSteel_06.TIF
   Save masked image with red background: Mask_Enh_StainlessSteel_06.TIF
Most common color: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/ Mask_Enh_StainlessSteel_06.TIF
    Main color from image: Mask_Enh_StainlessSteel_06.TIF
...  List without excluded colors
Count ocurrencies for color
      4 Most common colors: [((232, 229, 223), 1), ((230, 229, 223), 1), ((230, 227, 223), 1), ((228, 227, 223), 1)]
      Read color name: (232, 229, 223)
      Main Color file: Mask_Enh_StainlessSteel_06.TIF  RGB: [((232, 229, 223), 1)] (232, 229, 223)  Color name: gainsboro  Hex: #e8e5df
StainlessSteel  Size: (2048, 1536)  Format: TIFF  Mode: RGB
          Sum array: 194.59016  Sum Ch 0: 66.436627  Sum Ch 1: 66.436627  Sum Ch 2: 61.716906
          Histog   : 94.37184   N.List elem: 768  Max: 314171 Idx Max: 12   Min: 0 Idx Min: 254
          Color    : gainsboro    RGB   : (232, 229, 223)    Hex color: #e8e5df   Dec Color: 15263199
          Extremes : ((1, 255), (1, 255), (0, 254)) Med Extremes: 127.66666666666667
          Percentage R: 34.14182248475463   Percentage G: 31.71635503049075   Percentage B: 31.71635503049075
File Start: 2019-06-01 18:44:50.912114  Finish: 2019-06-01 18:44:59.664036   File Time: 0:00:08.751922
Process Start: 2019-06-01 18:34:38.342512  Finish: 2019-06-01 18:44:59.664036   Global Time: 0:10:21.321524

#list_dec_back ordered
order_list_dec = sorted(list_dec_back, key=int) 
#order_list_dec
#list_non_back 

'''
TESTS
# Read all list to see the color - obtain RGB from int
for x in order_list_dec:
    #print(x)
    # Get RGB from INT
    xrgb = getRGBfromI(x)
    #print('Int:, x,'  RGB: ',xrgb)
    xt_color_name , hexdc = get_rgb_color_name(xrgb)
    print('Int:', x,'  RGB: ', xrgb, xt_color_name)
'''          

"\nTESTS\n# Read all list to see the color - obtain RGB from int\nfor x in order_list_dec:\n    #print(x)\n    # Get RGB from INT\n    xrgb = getRGBfromI(x)\n    #print('Int:, x,'  RGB: ',xrgb)\n    xt_color_name , hexdc = get_rgb_color_name(xrgb)\n    print('Int:', x,'  RGB: ', xrgb, xt_color_name)\n"

df = pd.DataFrame(df_image,columns=['Folder','File','Material','Size','Format','Mode',
                                    'All_Bands', 'Sum_Ch0','Sum_Ch1','Sum_Ch2',
                                    'Histogram','Number_elements',
                                    'Color','Color_RGB', 'Color_hex','Color_dec','Med_Extrems',
                                    'Max_Histog', 'Idx_Max_Histog','Min_Histog', 'Idx_Min_Histog',
                                    'perc_R', 'perc_G', 'perc_B'])
df.head(10)

	Folder	File	Material	Size	Format	Mode	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	...	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	imagedata08	Aluminum_01.TIF	Aluminum	(2048, 1536)	TIFF	RGB	225.341887	76.708445	76.708445	71.924997	...	#eae5df	15394271	109.333333	251838	9	0	208	34.040917	34.040917	31.918166
1	imagedata08	Aluminum_02.TIF	Aluminum	(2048, 1536)	TIFF	RGB	232.768793	79.130088	79.130088	74.508617	...	#e6e5df	15132127	106.166667	244041	11	0	211	33.995145	33.995145	32.009711
2	imagedata08	Aluminum_03.TIF	Aluminum	(2048, 1536)	TIFF	RGB	281.688117	95.449458	95.449458	90.789201	...	#eae5df	15394271	125.666667	246108	11	0	247	33.884801	33.884801	32.230398
3	imagedata08	Aluminum_04.TIF	Aluminum	(2048, 1536)	TIFF	RGB	294.870824	99.847205	99.847205	95.176414	...	#e9e5df	15328735	127.833333	239672	11	0	255	33.861338	33.861338	32.277325
4	imagedata08	Brass_01.TIF	Brass	(2048, 1536)	TIFF	RGB	115.591735	40.112067	40.112067	35.367601	...	#eae5df	15394271	127.666667	310755	11	0	218	34.701501	34.701501	30.596998
5	imagedata08	Brass_02.TIF	Brass	(2048, 1536)	TIFF	RGB	115.114431	39.914903	39.914903	35.284625	...	#e6e5df	15132127	127.666667	303759	11	0	252	34.674109	34.674109	30.651782
6	imagedata08	Brass_04.TIF	Brass	(2048, 1536)	TIFF	RGB	112.513003	39.036133	39.036133	34.440737	...	#e6e5df	15132127	127.666667	303654	10	0	254	34.694775	34.694775	30.610450
7	imagedata08	Brass_05.TIF	Brass	(2048, 1536)	TIFF	RGB	115.000094	39.918248	39.918248	35.163598	...	#e8e5df	15263199	127.666667	304881	11	0	247	34.711492	34.711492	30.577017
8	imagedata08	Brass_06.TIF	Brass	(2048, 1536)	TIFF	RGB	124.310095	42.983402	42.983402	38.343291	...	#e5e5df	15066591	127.666667	317338	11	0	225	34.577563	34.577563	30.844873
9	imagedata08	Brass_07.TIF	Brass	(2048, 1536)	TIFF	RGB	122.271733	42.348619	42.348619	37.574495	...	#ebe5df	15459807	110.500000	318513	12	0	202	34.634840	34.634840	30.730320

10 rows × 24 columns

# Delete junk records
df = df[df.Material != 'MASK']
df = df[df.Material != 'Enh']
df

	Folder	File	Material	Size	Format	Mode	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	...	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	imagedata08	Aluminum_01.TIF	Aluminum	(2048, 1536)	TIFF	RGB	225.341887	76.708445	76.708445	71.924997	...	#eae5df	15394271	109.333333	251838	9	0	208	34.040917	34.040917	31.918166
1	imagedata08	Aluminum_02.TIF	Aluminum	(2048, 1536)	TIFF	RGB	232.768793	79.130088	79.130088	74.508617	...	#e6e5df	15132127	106.166667	244041	11	0	211	33.995145	33.995145	32.009711
2	imagedata08	Aluminum_03.TIF	Aluminum	(2048, 1536)	TIFF	RGB	281.688117	95.449458	95.449458	90.789201	...	#eae5df	15394271	125.666667	246108	11	0	247	33.884801	33.884801	32.230398
3	imagedata08	Aluminum_04.TIF	Aluminum	(2048, 1536)	TIFF	RGB	294.870824	99.847205	99.847205	95.176414	...	#e9e5df	15328735	127.833333	239672	11	0	255	33.861338	33.861338	32.277325
4	imagedata08	Brass_01.TIF	Brass	(2048, 1536)	TIFF	RGB	115.591735	40.112067	40.112067	35.367601	...	#eae5df	15394271	127.666667	310755	11	0	218	34.701501	34.701501	30.596998
5	imagedata08	Brass_02.TIF	Brass	(2048, 1536)	TIFF	RGB	115.114431	39.914903	39.914903	35.284625	...	#e6e5df	15132127	127.666667	303759	11	0	252	34.674109	34.674109	30.651782
6	imagedata08	Brass_04.TIF	Brass	(2048, 1536)	TIFF	RGB	112.513003	39.036133	39.036133	34.440737	...	#e6e5df	15132127	127.666667	303654	10	0	254	34.694775	34.694775	30.610450
7	imagedata08	Brass_05.TIF	Brass	(2048, 1536)	TIFF	RGB	115.000094	39.918248	39.918248	35.163598	...	#e8e5df	15263199	127.666667	304881	11	0	247	34.711492	34.711492	30.577017
8	imagedata08	Brass_06.TIF	Brass	(2048, 1536)	TIFF	RGB	124.310095	42.983402	42.983402	38.343291	...	#e5e5df	15066591	127.666667	317338	11	0	225	34.577563	34.577563	30.844873
9	imagedata08	Brass_07.TIF	Brass	(2048, 1536)	TIFF	RGB	122.271733	42.348619	42.348619	37.574495	...	#ebe5df	15459807	110.500000	318513	12	0	202	34.634840	34.634840	30.730320
10	imagedata08	Brass_08.TIF	Brass	(2048, 1536)	TIFF	RGB	124.488185	43.045525	43.045525	38.397135	...	#e5e5df	15066591	109.833333	320231	11	0	206	34.578000	34.578000	30.843999
11	imagedata08	CopperWire_01.TIF	CopperWire	(2048, 1536)	TIFF	RGB	165.883977	56.808970	56.808970	52.266037	...	#e5e5df	15066591	78.666667	327884	10	0	156	34.246207	34.246207	31.507586
12	imagedata08	CopperWire_02.TIF	CopperWire	(2048, 1536)	TIFF	RGB	181.932704	62.185989	62.185989	57.560726	...	#e5e5df	15066591	78.666667	303195	9	0	156	34.180764	34.180764	31.638471
13	imagedata08	CopperWire_03.TIF	CopperWire	(2048, 1536)	TIFF	RGB	202.037768	68.887443	68.887443	64.262882	...	#e5e5df	15066591	80.166667	295451	9	0	157	34.096320	34.096320	31.807361
14	imagedata08	CopperWire_04.TIF	CopperWire	(2048, 1536)	TIFF	RGB	531.395758	178.691930	178.691930	174.011898	...	#a9a59f	11117983	72.500000	92231	9	0	131	33.626902	33.626902	32.746196
15	imagedata08	CopperWire_05.TIF	CopperWire	(2048, 1536)	TIFF	RGB	535.292923	179.992400	179.992400	175.308123	...	#a9a59f	11117983	75.333333	85458	9	0	131	33.625029	33.625029	32.749942
16	imagedata08	Copper_01.TIF	Copper	(2048, 1536)	TIFF	RGB	147.616898	50.731603	50.731603	46.153692	...	#e5e5df	15066591	84.833333	318670	9	0	153	34.367070	34.367070	31.265860
17	imagedata08	Copper_02.TIF	Copper	(2048, 1536)	TIFF	RGB	142.227555	49.009888	49.009888	44.207779	...	#e7e5df	15197663	89.833333	321043	10	0	160	34.458785	34.458785	31.082429
18	imagedata08	Copper_03.TIF	Copper	(2048, 1536)	TIFF	RGB	130.530910	45.105093	45.105093	40.320724	...	#e6e5df	15132127	84.000000	309376	11	0	162	34.555105	34.555105	30.889790
19	imagedata08	Copper_04.TIF	Copper	(2048, 1536)	TIFF	RGB	163.031541	55.942568	55.942568	51.146405	...	#e5e5df	15066591	78.166667	322371	10	0	155	34.313954	34.313954	31.372092
20	imagedata08	Copper_05.TIF	Copper	(2048, 1536)	TIFF	RGB	156.115532	53.638529	53.638529	48.838474	...	#e6e5df	15132127	78.166667	329941	10	0	155	34.358227	34.358227	31.283546
21	imagedata08	Copper_06.TIF	Copper	(2048, 1536)	TIFF	RGB	137.346139	47.383762	47.383762	42.578615	...	#e6e5df	15132127	77.166667	340247	10	0	153	34.499522	34.499522	31.000955
22	imagedata08	Copper_07.TIF	Copper	(2048, 1536)	TIFF	RGB	119.949182	41.568840	41.568840	36.811502	...	#c6c5bf	13026751	85.333333	303151	11	0	131	34.655376	34.655376	30.689248
23	imagedata08	Copper_08.TIF	Copper	(2048, 1536)	TIFF	RGB	119.562843	41.439866	41.439866	36.683111	...	#e4e3df	15000543	91.666667	305393	9	0	131	34.659485	34.659485	30.681029
29	imagedata08	Iron_01.TIF	Iron	(2048, 1536)	TIFF	RGB	264.943069	89.816614	89.816614	85.309841	...	#e6e5df	15132127	79.500000	233823	10	0	157	33.900345	33.900345	32.199310
30	imagedata08	Iron_02.TIF	Iron	(2048, 1536)	TIFF	RGB	225.696231	76.731879	76.731879	72.232473	...	#e4e3df	15000543	79.833333	249211	10	0	159	33.997856	33.997856	32.004289
31	imagedata08	Iron_03.TIF	Iron	(2048, 1536)	TIFF	RGB	305.481411	103.468318	103.468318	98.544775	...	#e8e5df	15263199	81.666667	212646	11	0	158	33.870577	33.870577	32.258845
32	imagedata08	Iron_04.TIF	Iron	(2048, 1536)	TIFF	RGB	300.732499	101.887277	101.887277	96.957945	...	#e6e5df	15132127	81.833333	216346	11	0	160	33.879703	33.879703	32.240594
33	imagedata08	Iron_05.TIF	Iron	(2048, 1536)	TIFF	RGB	204.729830	69.866108	69.866108	64.997614	...	#f9e5df	16377311	78.500000	247280	9	0	155	34.126003	34.126003	31.747994
34	imagedata08	Iron_06.TIF	Iron	(2048, 1536)	TIFF	RGB	214.413784	73.088087	73.088087	68.237610	...	#e6e5df	15132127	84.333333	230280	8	0	151	34.087401	34.087401	31.825197
35	imagedata08	Iron_07.TIF	Iron	(2048, 1536)	TIFF	RGB	216.771872	73.841591	73.841591	69.088690	...	#e9e5df	15328735	86.833333	231255	10	0	155	34.064194	34.064194	31.871612
36	imagedata08	Iron_08.TIF	Iron	(2048, 1536)	TIFF	RGB	203.316977	69.359148	69.359148	64.598681	...	#f3e5df	15984095	116.333333	232920	10	0	154	34.113801	34.113801	31.772399
42	imagedata08	PaintedIron_01.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	155.320928	53.267878	53.267878	48.785172	...	#f6e5df	16180703	77.166667	345478	9	0	149	34.295364	34.295364	31.409272
43	imagedata08	PaintedIron_02.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	150.476603	51.651124	51.651124	47.174355	...	#eae5df	15394271	75.833333	353839	8	0	145	34.325020	34.325020	31.349960
44	imagedata08	PaintedIron_03.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	142.346993	49.095977	49.095977	44.155039	...	#ebe5df	15459807	73.500000	356636	8	0	142	34.490351	34.490351	31.019299
45	imagedata08	PaintedIron_04.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	143.721838	49.551402	49.551402	44.619034	...	#ebe5df	15459807	73.000000	371011	7	0	136	34.477295	34.477295	31.045410
46	imagedata08	PaintedIron_05.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	162.789726	55.806045	55.806045	51.177636	...	#f3e5df	15984095	76.000000	352642	8	0	141	34.281061	34.281061	31.437878
47	imagedata08	PaintedIron_06.TIF	PaintedIron	(2048, 1536)	TIFF	RGB	148.261640	51.008323	51.008323	46.244994	...	#f3e5df	15984095	76.333333	339650	7	0	143	34.404262	34.404262	31.191476
48	imagedata08	StainlessSteel_01.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	280.133436	94.878361	94.878361	90.376714	...	#e9e5df	15328735	127.833333	263983	10	0	255	33.868988	33.868988	32.262023
49	imagedata08	StainlessSteel_02.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	245.591395	83.361354	83.361354	78.868687	...	#f4e5df	16049631	127.833333	284112	10	0	255	33.943109	33.943109	32.113783
50	imagedata08	StainlessSteel_03.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	208.979553	71.308299	71.308299	66.362955	...	#eae5df	15394271	127.833333	297745	11	0	255	34.122142	34.122142	31.755717
51	imagedata08	StainlessSteel_04.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	247.456815	84.128099	84.128099	79.200617	...	#eae5df	15394271	127.833333	296137	12	0	240	33.997083	33.997083	32.005834
52	imagedata08	StainlessSteel_05.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	214.909839	73.279863	73.279863	68.350113	...	#ebe5df	15459807	127.666667	304852	11	0	254	34.097956	34.097956	31.804087
53	imagedata08	StainlessSteel_06.TIF	StainlessSteel	(2048, 1536)	TIFF	RGB	194.590160	66.436627	66.436627	61.716906	...	#e8e5df	15263199	127.666667	314171	12	0	254	34.141822	34.141822	31.716355

44 rows × 24 columns

Write statistics in excel book

# Verify my current folder
path = mypath + r"/upt_data.xlsx"
print('Write statistics into file :', path)

# Block to Read excel old excel file
book = load_workbook(path)
writer = pd.ExcelWriter(path, engine = 'openpyxl')
writer.book = book
# ------------------------

# Write statistics into excel file
#writer = pd.ExcelWriter(path, engine = 'xlsxwriter') # only for new excelfile
df.to_excel(writer, sheet_name = folder)
writer.save()
writer.close()

Write statistics into file : C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/upt_data.xlsx

Plot

df_plot = pd.DataFrame(df, columns=["Material", "All_Bands", "Sum_Ch0", "Sum_Ch1", "Sum_Ch2",
                                    "Color", "Color_RGB", "Color_hex","Color_dec",
                                    "Med_Extrems", "Max_Histog", "Idx_Max_Histog","Min_Histog",
                                    "Idx_Min_Histog","perc_R", "perc_G", "perc_B"])
df_plot

	Material	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color	Color_RGB	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	Aluminum	225.341887	76.708445	76.708445	71.924997	gainsboro	(234, 229, 223)	#eae5df	15394271	109.333333	251838	9	0	208	34.040917	34.040917	31.918166
1	Aluminum	232.768793	79.130088	79.130088	74.508617	gainsboro	(230, 229, 223)	#e6e5df	15132127	106.166667	244041	11	0	211	33.995145	33.995145	32.009711
2	Aluminum	281.688117	95.449458	95.449458	90.789201	gainsboro	(234, 229, 223)	#eae5df	15394271	125.666667	246108	11	0	247	33.884801	33.884801	32.230398
3	Aluminum	294.870824	99.847205	99.847205	95.176414	gainsboro	(233, 229, 223)	#e9e5df	15328735	127.833333	239672	11	0	255	33.861338	33.861338	32.277325
4	Brass	115.591735	40.112067	40.112067	35.367601	gainsboro	(234, 229, 223)	#eae5df	15394271	127.666667	310755	11	0	218	34.701501	34.701501	30.596998
5	Brass	115.114431	39.914903	39.914903	35.284625	gainsboro	(230, 229, 223)	#e6e5df	15132127	127.666667	303759	11	0	252	34.674109	34.674109	30.651782
6	Brass	112.513003	39.036133	39.036133	34.440737	gainsboro	(230, 229, 223)	#e6e5df	15132127	127.666667	303654	10	0	254	34.694775	34.694775	30.610450
7	Brass	115.000094	39.918248	39.918248	35.163598	gainsboro	(232, 229, 223)	#e8e5df	15263199	127.666667	304881	11	0	247	34.711492	34.711492	30.577017
8	Brass	124.310095	42.983402	42.983402	38.343291	gainsboro	(229, 229, 223)	#e5e5df	15066591	127.666667	317338	11	0	225	34.577563	34.577563	30.844873
9	Brass	122.271733	42.348619	42.348619	37.574495	gainsboro	(235, 229, 223)	#ebe5df	15459807	110.500000	318513	12	0	202	34.634840	34.634840	30.730320
10	Brass	124.488185	43.045525	43.045525	38.397135	gainsboro	(229, 229, 223)	#e5e5df	15066591	109.833333	320231	11	0	206	34.578000	34.578000	30.843999
11	CopperWire	165.883977	56.808970	56.808970	52.266037	gainsboro	(229, 229, 223)	#e5e5df	15066591	78.666667	327884	10	0	156	34.246207	34.246207	31.507586
12	CopperWire	181.932704	62.185989	62.185989	57.560726	gainsboro	(229, 229, 223)	#e5e5df	15066591	78.666667	303195	9	0	156	34.180764	34.180764	31.638471
13	CopperWire	202.037768	68.887443	68.887443	64.262882	gainsboro	(229, 229, 223)	#e5e5df	15066591	80.166667	295451	9	0	157	34.096320	34.096320	31.807361
14	CopperWire	531.395758	178.691930	178.691930	174.011898	darkgray	(169, 165, 159)	#a9a59f	11117983	72.500000	92231	9	0	131	33.626902	33.626902	32.746196
15	CopperWire	535.292923	179.992400	179.992400	175.308123	darkgray	(169, 165, 159)	#a9a59f	11117983	75.333333	85458	9	0	131	33.625029	33.625029	32.749942
16	Copper	147.616898	50.731603	50.731603	46.153692	gainsboro	(229, 229, 223)	#e5e5df	15066591	84.833333	318670	9	0	153	34.367070	34.367070	31.265860
17	Copper	142.227555	49.009888	49.009888	44.207779	gainsboro	(231, 229, 223)	#e7e5df	15197663	89.833333	321043	10	0	160	34.458785	34.458785	31.082429
18	Copper	130.530910	45.105093	45.105093	40.320724	gainsboro	(230, 229, 223)	#e6e5df	15132127	84.000000	309376	11	0	162	34.555105	34.555105	30.889790
19	Copper	163.031541	55.942568	55.942568	51.146405	gainsboro	(229, 229, 223)	#e5e5df	15066591	78.166667	322371	10	0	155	34.313954	34.313954	31.372092
20	Copper	156.115532	53.638529	53.638529	48.838474	gainsboro	(230, 229, 223)	#e6e5df	15132127	78.166667	329941	10	0	155	34.358227	34.358227	31.283546
21	Copper	137.346139	47.383762	47.383762	42.578615	gainsboro	(230, 229, 223)	#e6e5df	15132127	77.166667	340247	10	0	153	34.499522	34.499522	31.000955
22	Copper	119.949182	41.568840	41.568840	36.811502	silver	(198, 197, 191)	#c6c5bf	13026751	85.333333	303151	11	0	131	34.655376	34.655376	30.689248
23	Copper	119.562843	41.439866	41.439866	36.683111	gainsboro	(228, 227, 223)	#e4e3df	15000543	91.666667	305393	9	0	131	34.659485	34.659485	30.681029
29	Iron	264.943069	89.816614	89.816614	85.309841	gainsboro	(230, 229, 223)	#e6e5df	15132127	79.500000	233823	10	0	157	33.900345	33.900345	32.199310
30	Iron	225.696231	76.731879	76.731879	72.232473	gainsboro	(228, 227, 223)	#e4e3df	15000543	79.833333	249211	10	0	159	33.997856	33.997856	32.004289
31	Iron	305.481411	103.468318	103.468318	98.544775	gainsboro	(232, 229, 223)	#e8e5df	15263199	81.666667	212646	11	0	158	33.870577	33.870577	32.258845
32	Iron	300.732499	101.887277	101.887277	96.957945	gainsboro	(230, 229, 223)	#e6e5df	15132127	81.833333	216346	11	0	160	33.879703	33.879703	32.240594
33	Iron	204.729830	69.866108	69.866108	64.997614	mistyrose	(249, 229, 223)	#f9e5df	16377311	78.500000	247280	9	0	155	34.126003	34.126003	31.747994
34	Iron	214.413784	73.088087	73.088087	68.237610	gainsboro	(230, 229, 223)	#e6e5df	15132127	84.333333	230280	8	0	151	34.087401	34.087401	31.825197
35	Iron	216.771872	73.841591	73.841591	69.088690	gainsboro	(233, 229, 223)	#e9e5df	15328735	86.833333	231255	10	0	155	34.064194	34.064194	31.871612
36	Iron	203.316977	69.359148	69.359148	64.598681	mistyrose	(243, 229, 223)	#f3e5df	15984095	116.333333	232920	10	0	154	34.113801	34.113801	31.772399
42	PaintedIron	155.320928	53.267878	53.267878	48.785172	mistyrose	(246, 229, 223)	#f6e5df	16180703	77.166667	345478	9	0	149	34.295364	34.295364	31.409272
43	PaintedIron	150.476603	51.651124	51.651124	47.174355	gainsboro	(234, 229, 223)	#eae5df	15394271	75.833333	353839	8	0	145	34.325020	34.325020	31.349960
44	PaintedIron	142.346993	49.095977	49.095977	44.155039	gainsboro	(235, 229, 223)	#ebe5df	15459807	73.500000	356636	8	0	142	34.490351	34.490351	31.019299
45	PaintedIron	143.721838	49.551402	49.551402	44.619034	gainsboro	(235, 229, 223)	#ebe5df	15459807	73.000000	371011	7	0	136	34.477295	34.477295	31.045410
46	PaintedIron	162.789726	55.806045	55.806045	51.177636	mistyrose	(243, 229, 223)	#f3e5df	15984095	76.000000	352642	8	0	141	34.281061	34.281061	31.437878
47	PaintedIron	148.261640	51.008323	51.008323	46.244994	mistyrose	(243, 229, 223)	#f3e5df	15984095	76.333333	339650	7	0	143	34.404262	34.404262	31.191476
48	StainlessSteel	280.133436	94.878361	94.878361	90.376714	gainsboro	(233, 229, 223)	#e9e5df	15328735	127.833333	263983	10	0	255	33.868988	33.868988	32.262023
49	StainlessSteel	245.591395	83.361354	83.361354	78.868687	mistyrose	(244, 229, 223)	#f4e5df	16049631	127.833333	284112	10	0	255	33.943109	33.943109	32.113783
50	StainlessSteel	208.979553	71.308299	71.308299	66.362955	gainsboro	(234, 229, 223)	#eae5df	15394271	127.833333	297745	11	0	255	34.122142	34.122142	31.755717
51	StainlessSteel	247.456815	84.128099	84.128099	79.200617	gainsboro	(234, 229, 223)	#eae5df	15394271	127.833333	296137	12	0	240	33.997083	33.997083	32.005834
52	StainlessSteel	214.909839	73.279863	73.279863	68.350113	gainsboro	(235, 229, 223)	#ebe5df	15459807	127.666667	304852	11	0	254	34.097956	34.097956	31.804087
53	StainlessSteel	194.590160	66.436627	66.436627	61.716906	gainsboro	(232, 229, 223)	#e8e5df	15263199	127.666667	314171	12	0	254	34.141822	34.141822	31.716355

# Adjust values to plot
df_plot.Sum_Ch0        = df_plot.Sum_Ch0 + 500 # to have diference lines during plot
df_plot.Sum_Ch1        = df_plot.Sum_Ch1 + 1000
df_plot.Sum_Ch2        = df_plot.Sum_Ch2 + 1500
df_plot.All_Bands      = df_plot.All_Bands + 2000

df_plot.Color_dec      = df_plot.Color_dec / 1000
df_plot.Color_dec      = df_plot.Color_dec - 10000
df_plot.Med_Extrems    = df_plot.Med_Extrems + 500
df_plot.Max_Histog     = df_plot.Max_Histog / 1000
df_plot.Idx_Max_Histog = df_plot.Idx_Max_Histog + 1000
df_plot.Min_Histog     = df_plot.Min_Histog * 100
df_plot.Idx_Min_Histog = df_plot.Idx_Min_Histog * 10

df_plot.perc_R = df_plot.perc_R + 1000
df_plot.perc_G = df_plot.perc_G + 1100
df_plot.perc_B = df_plot.perc_B + 1200

df_plot

	Material	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color	Color_RGB	Color_hex	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
0	Aluminum	2225.341887	576.708445	1076.708445	1571.924997	gainsboro	(234, 229, 223)	#eae5df	5394.271	609.333333	251.838	1009	0	2080	1034.040917	1134.040917	1231.918166
1	Aluminum	2232.768793	579.130088	1079.130088	1574.508617	gainsboro	(230, 229, 223)	#e6e5df	5132.127	606.166667	244.041	1011	0	2110	1033.995145	1133.995145	1232.009711
2	Aluminum	2281.688117	595.449458	1095.449458	1590.789201	gainsboro	(234, 229, 223)	#eae5df	5394.271	625.666667	246.108	1011	0	2470	1033.884801	1133.884801	1232.230398
3	Aluminum	2294.870824	599.847205	1099.847205	1595.176414	gainsboro	(233, 229, 223)	#e9e5df	5328.735	627.833333	239.672	1011	0	2550	1033.861338	1133.861338	1232.277325
4	Brass	2115.591735	540.112067	1040.112067	1535.367601	gainsboro	(234, 229, 223)	#eae5df	5394.271	627.666667	310.755	1011	0	2180	1034.701501	1134.701501	1230.596998
5	Brass	2115.114431	539.914903	1039.914903	1535.284625	gainsboro	(230, 229, 223)	#e6e5df	5132.127	627.666667	303.759	1011	0	2520	1034.674109	1134.674109	1230.651782
6	Brass	2112.513003	539.036133	1039.036133	1534.440737	gainsboro	(230, 229, 223)	#e6e5df	5132.127	627.666667	303.654	1010	0	2540	1034.694775	1134.694775	1230.610450
7	Brass	2115.000094	539.918248	1039.918248	1535.163598	gainsboro	(232, 229, 223)	#e8e5df	5263.199	627.666667	304.881	1011	0	2470	1034.711492	1134.711492	1230.577017
8	Brass	2124.310095	542.983402	1042.983402	1538.343291	gainsboro	(229, 229, 223)	#e5e5df	5066.591	627.666667	317.338	1011	0	2250	1034.577563	1134.577563	1230.844873
9	Brass	2122.271733	542.348619	1042.348619	1537.574495	gainsboro	(235, 229, 223)	#ebe5df	5459.807	610.500000	318.513	1012	0	2020	1034.634840	1134.634840	1230.730320
10	Brass	2124.488185	543.045525	1043.045525	1538.397135	gainsboro	(229, 229, 223)	#e5e5df	5066.591	609.833333	320.231	1011	0	2060	1034.578000	1134.578000	1230.843999
11	CopperWire	2165.883977	556.808970	1056.808970	1552.266037	gainsboro	(229, 229, 223)	#e5e5df	5066.591	578.666667	327.884	1010	0	1560	1034.246207	1134.246207	1231.507586
12	CopperWire	2181.932704	562.185989	1062.185989	1557.560726	gainsboro	(229, 229, 223)	#e5e5df	5066.591	578.666667	303.195	1009	0	1560	1034.180764	1134.180764	1231.638471
13	CopperWire	2202.037768	568.887443	1068.887443	1564.262882	gainsboro	(229, 229, 223)	#e5e5df	5066.591	580.166667	295.451	1009	0	1570	1034.096320	1134.096320	1231.807361
14	CopperWire	2531.395758	678.691930	1178.691930	1674.011898	darkgray	(169, 165, 159)	#a9a59f	1117.983	572.500000	92.231	1009	0	1310	1033.626902	1133.626902	1232.746196
15	CopperWire	2535.292923	679.992400	1179.992400	1675.308123	darkgray	(169, 165, 159)	#a9a59f	1117.983	575.333333	85.458	1009	0	1310	1033.625029	1133.625029	1232.749942
16	Copper	2147.616898	550.731603	1050.731603	1546.153692	gainsboro	(229, 229, 223)	#e5e5df	5066.591	584.833333	318.670	1009	0	1530	1034.367070	1134.367070	1231.265860
17	Copper	2142.227555	549.009888	1049.009888	1544.207779	gainsboro	(231, 229, 223)	#e7e5df	5197.663	589.833333	321.043	1010	0	1600	1034.458785	1134.458785	1231.082429
18	Copper	2130.530910	545.105093	1045.105093	1540.320724	gainsboro	(230, 229, 223)	#e6e5df	5132.127	584.000000	309.376	1011	0	1620	1034.555105	1134.555105	1230.889790
19	Copper	2163.031541	555.942568	1055.942568	1551.146405	gainsboro	(229, 229, 223)	#e5e5df	5066.591	578.166667	322.371	1010	0	1550	1034.313954	1134.313954	1231.372092
20	Copper	2156.115532	553.638529	1053.638529	1548.838474	gainsboro	(230, 229, 223)	#e6e5df	5132.127	578.166667	329.941	1010	0	1550	1034.358227	1134.358227	1231.283546
21	Copper	2137.346139	547.383762	1047.383762	1542.578615	gainsboro	(230, 229, 223)	#e6e5df	5132.127	577.166667	340.247	1010	0	1530	1034.499522	1134.499522	1231.000955
22	Copper	2119.949182	541.568840	1041.568840	1536.811502	silver	(198, 197, 191)	#c6c5bf	3026.751	585.333333	303.151	1011	0	1310	1034.655376	1134.655376	1230.689248
23	Copper	2119.562843	541.439866	1041.439866	1536.683111	gainsboro	(228, 227, 223)	#e4e3df	5000.543	591.666667	305.393	1009	0	1310	1034.659485	1134.659485	1230.681029
29	Iron	2264.943069	589.816614	1089.816614	1585.309841	gainsboro	(230, 229, 223)	#e6e5df	5132.127	579.500000	233.823	1010	0	1570	1033.900345	1133.900345	1232.199310
30	Iron	2225.696231	576.731879	1076.731879	1572.232473	gainsboro	(228, 227, 223)	#e4e3df	5000.543	579.833333	249.211	1010	0	1590	1033.997856	1133.997856	1232.004289
31	Iron	2305.481411	603.468318	1103.468318	1598.544775	gainsboro	(232, 229, 223)	#e8e5df	5263.199	581.666667	212.646	1011	0	1580	1033.870577	1133.870577	1232.258845
32	Iron	2300.732499	601.887277	1101.887277	1596.957945	gainsboro	(230, 229, 223)	#e6e5df	5132.127	581.833333	216.346	1011	0	1600	1033.879703	1133.879703	1232.240594
33	Iron	2204.729830	569.866108	1069.866108	1564.997614	mistyrose	(249, 229, 223)	#f9e5df	6377.311	578.500000	247.280	1009	0	1550	1034.126003	1134.126003	1231.747994
34	Iron	2214.413784	573.088087	1073.088087	1568.237610	gainsboro	(230, 229, 223)	#e6e5df	5132.127	584.333333	230.280	1008	0	1510	1034.087401	1134.087401	1231.825197
35	Iron	2216.771872	573.841591	1073.841591	1569.088690	gainsboro	(233, 229, 223)	#e9e5df	5328.735	586.833333	231.255	1010	0	1550	1034.064194	1134.064194	1231.871612
36	Iron	2203.316977	569.359148	1069.359148	1564.598681	mistyrose	(243, 229, 223)	#f3e5df	5984.095	616.333333	232.920	1010	0	1540	1034.113801	1134.113801	1231.772399
42	PaintedIron	2155.320928	553.267878	1053.267878	1548.785172	mistyrose	(246, 229, 223)	#f6e5df	6180.703	577.166667	345.478	1009	0	1490	1034.295364	1134.295364	1231.409272
43	PaintedIron	2150.476603	551.651124	1051.651124	1547.174355	gainsboro	(234, 229, 223)	#eae5df	5394.271	575.833333	353.839	1008	0	1450	1034.325020	1134.325020	1231.349960
44	PaintedIron	2142.346993	549.095977	1049.095977	1544.155039	gainsboro	(235, 229, 223)	#ebe5df	5459.807	573.500000	356.636	1008	0	1420	1034.490351	1134.490351	1231.019299
45	PaintedIron	2143.721838	549.551402	1049.551402	1544.619034	gainsboro	(235, 229, 223)	#ebe5df	5459.807	573.000000	371.011	1007	0	1360	1034.477295	1134.477295	1231.045410
46	PaintedIron	2162.789726	555.806045	1055.806045	1551.177636	mistyrose	(243, 229, 223)	#f3e5df	5984.095	576.000000	352.642	1008	0	1410	1034.281061	1134.281061	1231.437878
47	PaintedIron	2148.261640	551.008323	1051.008323	1546.244994	mistyrose	(243, 229, 223)	#f3e5df	5984.095	576.333333	339.650	1007	0	1430	1034.404262	1134.404262	1231.191476
48	StainlessSteel	2280.133436	594.878361	1094.878361	1590.376714	gainsboro	(233, 229, 223)	#e9e5df	5328.735	627.833333	263.983	1010	0	2550	1033.868988	1133.868988	1232.262023
49	StainlessSteel	2245.591395	583.361354	1083.361354	1578.868687	mistyrose	(244, 229, 223)	#f4e5df	6049.631	627.833333	284.112	1010	0	2550	1033.943109	1133.943109	1232.113783
50	StainlessSteel	2208.979553	571.308299	1071.308299	1566.362955	gainsboro	(234, 229, 223)	#eae5df	5394.271	627.833333	297.745	1011	0	2550	1034.122142	1134.122142	1231.755717
51	StainlessSteel	2247.456815	584.128099	1084.128099	1579.200617	gainsboro	(234, 229, 223)	#eae5df	5394.271	627.833333	296.137	1012	0	2400	1033.997083	1133.997083	1232.005834
52	StainlessSteel	2214.909839	573.279863	1073.279863	1568.350113	gainsboro	(235, 229, 223)	#ebe5df	5459.807	627.666667	304.852	1011	0	2540	1034.097956	1134.097956	1231.804087
53	StainlessSteel	2194.590160	566.436627	1066.436627	1561.716906	gainsboro	(232, 229, 223)	#e8e5df	5263.199	627.666667	314.171	1012	0	2540	1034.141822	1134.141822	1231.716355

df_plot.plot(y=["All_Bands","Sum_Ch0","Sum_Ch1", "Sum_Ch2","Color_dec","Med_Extrems"],
             figsize=(12,6), grid=True )

# Obtain legend (xticks) for X axis
loc_Array_sum = np.arange(len(df_plot.index))
# Position of X labels
xtick_loc = list(loc_Array_sum)  
# Name of x labels
xticks = list(df_plot.Material)
#-------

#plt.plot(df_plot.Array_sum)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(xtick_loc, df_plot.Material, rotation=90)
plt.xlabel('Material',fontsize=18)
plt.legend(loc='upper right', ncol=3, fancybox=True, shadow=True)
plt.savefig(folder+"_Line Graph all channels information.png")
plt.show()

df_plot.perc_R = df_plot.perc_R - 1000
df_plot.perc_G = df_plot.perc_G - 1100
df_plot.perc_B = df_plot.perc_B - 1200

df_plot.plot(y=["perc_R","perc_G","perc_B"],
             figsize=(12,6), grid=True )

# Obtain legend (xticks) for X axis
loc_Array_sum = np.arange(len(df_plot.index))
# Position of X labels
xtick_loc = list(loc_Array_sum)  
# Name of x labels
xticks = list(df_plot.Material)
#-------

#plt.plot(df_plot.Array_sum)
plt.title('Percentage R,G,B',fontsize=20)
plt.ylabel('Percentages R,G,B',fontsize=18)
plt.xticks(xtick_loc, df_plot.Material, rotation=90)
plt.xlabel('Material',fontsize=18)
plt.legend(loc='upper right', ncol=3, fancybox=True, shadow=True)
plt.savefig(folder+"_Line Graph Percentage RGB.png")
plt.show()

# Create pivot table
df_plot1 = df_plot.groupby('Material')['All_Bands', 'Sum_Ch0','Sum_Ch1','Sum_Ch2','Color_dec',
                                       'Med_Extrems', 'Max_Histog', 'Idx_Max_Histog','Min_Histog',
                                       'Idx_Min_Histog','perc_R','perc_G','perc_B'].mean()
df_plot1

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2258.667405	587.783799	1087.783799	1583.099807	5312.351000	617.250000	245.414750	1010.500000	0.0	2302.500000	-966.054450	-1066.054450	-1167.891100
Brass	2118.469897	541.051271	1041.051271	1536.367355	5216.387571	622.666667	311.304429	1011.000000	0.0	2291.428571	-965.346817	-1065.346817	-1169.306366
Copper	2139.547575	548.102519	1048.102519	1543.342538	4844.315000	583.645833	318.774000	1010.000000	0.0	1500.000000	-965.516559	-1065.516559	-1168.966881
CopperWire	2323.308626	609.313346	1109.313346	1604.681933	3487.147800	577.066667	220.843800	1009.200000	0.0	1462.000000	-966.044956	-1066.044956	-1167.910089
Iron	2242.010709	582.257378	1082.257378	1577.495954	5418.783000	586.104167	231.720125	1009.875000	0.0	1561.250000	-965.995015	-1065.995015	-1168.009970
PaintedIron	2150.486288	551.730125	1051.730125	1547.026038	5743.796333	575.305556	353.209333	1007.833333	0.0	1426.666667	-965.621108	-1065.621108	-1168.757784
StainlessSteel	2231.943533	578.898767	1078.898767	1574.145999	5481.652333	627.777778	293.500000	1011.000000	0.0	2521.666667	-965.971483	-1065.971483	-1168.057034

color = ['red','blue','green','orange','cyan','black','yellow']

df_All_Bands = pd.DataFrame(df_plot1.All_Bands)

df_All_Bands.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.savefig(folder+"_Sum of image matrix.png")
plt.show()

df_Max_Histog = pd.DataFrame(df_plot1.Max_Histog)

df_Max_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Max_Histog',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Max_Histog',fontsize=18)
plt.savefig(folder+"_Max_Histog.png")
plt.show()

df_perc = pd.DataFrame(df_plot1.perc_R)

df_perc.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH CHANNELS-Percentage R',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Max_Histog',fontsize=18)
plt.show()

loc_Array_sum = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location

loc_r = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location

#xtick_loc = list(loc_Array_sum) + list(loc_r) + list(loc_g) + list(loc_b)
#xticks = list(selected.keys())+ list(rejected.keys())
colors = ['darkred','red','green','blue','orange','cyan','black','yellow']
plt.figure(figsize=(12,5))

plt.bar(loc_r, df_plot1.perc_R, color='red', width=0.1, label='Band R')
plt.bar(loc_g, df_plot1.perc_G, color='green', width=0.1,label='Band G')
plt.bar(loc_b, df_plot1.perc_B, color='blue', width=0.1,label='Band B')

plt.title('Percentage R,G,B',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('RGB',fontsize=18)
plt.grid(True)
plt.xticks(xtick_loc, xticks, rotation=90)
plt.legend(bbox_to_anchor=(.8,0.8),\
    bbox_transform=plt.gcf().transFigure)
plt.savefig(folder+"_Bar Diagram_perc_RGB.png")
plt.show()

df_Idx_Min_Histog = pd.DataFrame(df_plot1.Idx_Min_Histog)

df_Idx_Min_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Idx_Min_Histog',fontsize=15)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Min_Histog',fontsize=18)
plt.savefig(folder+"_Idx_Min_Histogram.png")
plt.show()

df_Idx_Min_Histog = pd.DataFrame(df_plot1.Idx_Min_Histog)

df_Idx_Min_Histog.plot(kind='bar', y=0, color=color, legend=False, rot=0, figsize=(10,5))
plt.title('IMAGE WITH ALL CHANNELS-Idx_Min_Histog',fontsize=15)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Idx_Min_Histog',fontsize=18)
plt.show()

loc_Array_sum = np.arange(len(df_plot1.index))
xtick_loc = list(loc_Array_sum)  
xticks = list(df_plot1.index)

df_plot1.plot( y=["All_Bands","Sum_Ch0","Sum_Ch1", "Sum_Ch2","Color_dec","Med_Extrems"],
              figsize=(12,6), grid=True )
plt.xticks(xtick_loc, df_plot1.index, rotation=0)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.legend(loc='upper right', ncol=1, fancybox=True, shadow=True,bbox_to_anchor=(1.1, 1.05))
plt.savefig(folder+"_resume all channels.png")
plt.show()

df_plot1

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2258.667405	587.783799	1087.783799	1583.099807	5312.351000	617.250000	245.414750	1010.500000	0.0	2302.500000	-966.054450	-1066.054450	-1167.891100
Brass	2118.469897	541.051271	1041.051271	1536.367355	5216.387571	622.666667	311.304429	1011.000000	0.0	2291.428571	-965.346817	-1065.346817	-1169.306366
Copper	2139.547575	548.102519	1048.102519	1543.342538	4844.315000	583.645833	318.774000	1010.000000	0.0	1500.000000	-965.516559	-1065.516559	-1168.966881
CopperWire	2323.308626	609.313346	1109.313346	1604.681933	3487.147800	577.066667	220.843800	1009.200000	0.0	1462.000000	-966.044956	-1066.044956	-1167.910089
Iron	2242.010709	582.257378	1082.257378	1577.495954	5418.783000	586.104167	231.720125	1009.875000	0.0	1561.250000	-965.995015	-1065.995015	-1168.009970
PaintedIron	2150.486288	551.730125	1051.730125	1547.026038	5743.796333	575.305556	353.209333	1007.833333	0.0	1426.666667	-965.621108	-1065.621108	-1168.757784
StainlessSteel	2231.943533	578.898767	1078.898767	1574.145999	5481.652333	627.777778	293.500000	1011.000000	0.0	2521.666667	-965.971483	-1065.971483	-1168.057034

# Copy dataframe to arrange values
df_plot2 =  df_plot1.copy() 
df_plot2

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2258.667405	587.783799	1087.783799	1583.099807	5312.351000	617.250000	245.414750	1010.500000	0.0	2302.500000	-966.054450	-1066.054450	-1167.891100
Brass	2118.469897	541.051271	1041.051271	1536.367355	5216.387571	622.666667	311.304429	1011.000000	0.0	2291.428571	-965.346817	-1065.346817	-1169.306366
Copper	2139.547575	548.102519	1048.102519	1543.342538	4844.315000	583.645833	318.774000	1010.000000	0.0	1500.000000	-965.516559	-1065.516559	-1168.966881
CopperWire	2323.308626	609.313346	1109.313346	1604.681933	3487.147800	577.066667	220.843800	1009.200000	0.0	1462.000000	-966.044956	-1066.044956	-1167.910089
Iron	2242.010709	582.257378	1082.257378	1577.495954	5418.783000	586.104167	231.720125	1009.875000	0.0	1561.250000	-965.995015	-1065.995015	-1168.009970
PaintedIron	2150.486288	551.730125	1051.730125	1547.026038	5743.796333	575.305556	353.209333	1007.833333	0.0	1426.666667	-965.621108	-1065.621108	-1168.757784
StainlessSteel	2231.943533	578.898767	1078.898767	1574.145999	5481.652333	627.777778	293.500000	1011.000000	0.0	2521.666667	-965.971483	-1065.971483	-1168.057034

df_plot2.Med_Extrems    = df_plot2.Med_Extrems + 2000
df_plot2.Max_Histog     = df_plot2.Max_Histog  + 1500
df_plot2.Idx_Max_Histog = df_plot2.Idx_Max_Histog + 1000
df_plot2.Min_Histog     = df_plot2.Min_Histog + 500
df_plot2.Idx_Min_Histog = df_plot2.Idx_Min_Histog - 1000
df_plot2.head()

	All_Bands	Sum_Ch0	Sum_Ch1	Sum_Ch2	Color_dec	Med_Extrems	Max_Histog	Idx_Max_Histog	Min_Histog	Idx_Min_Histog	perc_R	perc_G	perc_B
Material
Aluminum	2258.667405	587.783799	1087.783799	1583.099807	5312.351000	2617.250000	1745.414750	2010.500	500.0	1302.500000	-966.054450	-1066.054450	-1167.891100
Brass	2118.469897	541.051271	1041.051271	1536.367355	5216.387571	2622.666667	1811.304429	2011.000	500.0	1291.428571	-965.346817	-1065.346817	-1169.306366
Copper	2139.547575	548.102519	1048.102519	1543.342538	4844.315000	2583.645833	1818.774000	2010.000	500.0	500.000000	-965.516559	-1065.516559	-1168.966881
CopperWire	2323.308626	609.313346	1109.313346	1604.681933	3487.147800	2577.066667	1720.843800	2009.200	500.0	462.000000	-966.044956	-1066.044956	-1167.910089
Iron	2242.010709	582.257378	1082.257378	1577.495954	5418.783000	2586.104167	1731.720125	2009.875	500.0	561.250000	-965.995015	-1065.995015	-1168.009970

loc_Array_sum = np.arange(len(df_plot2.index))
xtick_loc = list(loc_Array_sum)  
xticks = list(df_plot1.index)

df_plot2.plot( y=['Color_dec','Med_Extrems', 'Max_Histog', 'Idx_Max_Histog',
                  'Min_Histog','Idx_Min_Histog','perc_R','perc_G','perc_B'],figsize=(12,6), grid=True )
plt.xticks(xtick_loc, df_plot2.index, rotation=0)
plt.title('IMAGE WITH ALL CHANNELS - Color and Histogram',fontsize=18)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of Color and Histogram',fontsize=18)
plt.legend(loc='upper right', ncol=1, fancybox=True, shadow=True,bbox_to_anchor=(1.1, 1.05))
plt.savefig(folder+"_color_and_histogram.png")
plt.show()

# Create Xlabels
loc_Array_sum = np.arange(len(df_plot1.index))+0.0 # Offsetting the tick-label location
loc_r = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location

xtick_loc = list(loc_g)  
xticks = list(df_plot1.index)

# Plot

# Plot  Bar Graph
#df_plot1.plot(kind='bar', figsize=(12,5), grid=True, color='darkred',fontsize=18)
loc_Array_sum = np.arange(len(df_plot1.index))+0.0 # Offsetting the tick-label location
loc_b = np.arange(len(df_plot1.index))+0.1 # Offsetting the tick-label location
loc_g = np.arange(len(df_plot1.index))-0.0 # Offsetting the tick-label location
loc_r = np.arange(len(df_plot1.index))-0.1 # Offsetting the tick-label location

#xtick_loc = list(loc_Array_sum) + list(loc_r) + list(loc_g) + list(loc_b)
#xticks = list(selected.keys())+ list(rejected.keys())
colors = ['darkred','red','green','blue','orange','cyan','black','yellow']
plt.figure(figsize=(12,5))

plt.bar(loc_Array_sum, df_plot1.All_Bands, color=colors[0], width=0.1, label='All_Bands')
plt.bar(loc_r, df_plot1.Sum_Ch0, color=colors[1], width=0.1,label='Band R')
plt.bar(loc_g, df_plot1.Sum_Ch1, color=colors[2], width=0.1,label='Band G')
plt.bar(loc_b, df_plot1.Sum_Ch2, color=colors[3], width=0.1,label='Band B')

plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.grid(True)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(xtick_loc, xticks, rotation=0)
plt.legend(bbox_to_anchor=(.8,0.8),\
    bbox_transform=plt.gcf().transFigure)
plt.savefig(folder+"_all bands.png")
plt.show()

plt.figure(1)
plt.figure(figsize=(17, 4))
plt.tight_layout()
plt.subplot(231)
plt.title('IMAGE CHANNEL 0')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.Sum_Ch0, 'k--')

plt.subplot(232)
plt.title('IMAGE CHANNEL 1')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.Sum_Ch1,  'r--')

plt.subplot(233)
plt.title('IMAGE CHANNEL 2')
plt.xticks(rotation=45)
plt.plot(df_plot1.Sum_Ch2,  'g--')
plt.grid(True)
plt.suptitle('Sum Matrix of channels',fontsize=20,y=1.08)
#plt.tight_layout()
plt.subplots_adjust(top=0.8)
plt.savefig(folder+"_Sum Matrix of channels.png")
plt.show()

<Figure size 432x288 with 0 Axes>

# Pèrcentage of R,G,B
plt.figure(1)
plt.figure(figsize=(17, 4))
plt.tight_layout()
plt.subplot(231)
plt.title('IMAGE CHANNEL R')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.perc_R, 'r--')

plt.subplot(232)
plt.title('IMAGE CHANNEL G')
plt.xticks(rotation=45)
plt.grid(True)
plt.plot(df_plot1.perc_G,  'g--')

plt.subplot(233)
plt.title('IMAGE CHANNEL B')
plt.xticks(rotation=45)
plt.plot(df_plot1.perc_B,  'b--')
plt.grid(True)

plt.suptitle('Percentage of R,G,B',fontsize=20,y=1.08)
#plt.tight_layout()
plt.subplots_adjust(top=0.8)
plt.savefig(folder+'_Percentage_RGB.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

<Figure size 432x288 with 0 Axes>

# Plot channel based
plt.plot(df_plot1.All_Bands)
plt.title('IMAGE WITH ALL CHANNELS',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Sum of image matrix',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.savefig(folder+'_Sum_all_channels.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

# Plot based on color
plt.plot(df_plot1.Color_dec/1000)
plt.title('IMAGE WITH COLORS BASED',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of color image',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.show()

# Plot based on Extrems of the Bands
plt.plot(df_plot1.Med_Extrems/10)
plt.title('IMAGE WITH EXTREMS BANDS BASED',fontsize=20)
plt.xlabel('Material',fontsize=18)
plt.ylabel('Average of Extrems image',fontsize=18)
plt.xticks(rotation=45)
plt.grid(True)
plt.savefig(folder+'_color_based.png', bbox_inches='tight', pad_inches=0.0)
plt.show()

Create Histograms

https://www.cambridgeincolour.com/pt-br/tutoriais/histograms1.htm

https://www.cambridgeincolour.com/pt-br/tutoriais/image-noise.htm

http://www2.ic.uff.br/~aconci/aula-2-2015-AI.pdf

https://www.ic.unicamp.br/~ra144681/misc/files/ApostilaProcDeImagesParteI.pdf

histograma, também conhecido como distribuição de frequências ou diagrama das frequências, é a representação gráfica, em colunas (retângulos), de um conjunto de dados previamente tabulado e dividido em classes uniformes.

Histogramas:

O histograma de uma imagem cinza e uma funcao discreta h(l) (vetor) que produz o numero de ocorrencias de cada nıvel de cinza na imagem. O histograma normalizado h(l)/|DI | representa a distribuicao de probabilidade dos valores dos pixels.

Imagens claras possuem histogramas com altas concentracoes de pixels de alto brilho. Imagens escuras possuem histogramas com altas concentracoes de pixels de baixo brilho. O contraste maior esta associado a um grau maior de dispersao do histograma.

No caso de imagens multiespectrais, cada banda e´ requantizada em um certo numero de intervalos, de forma que o espaco de caracterısticas Zk ´e dividido em hipercubos (bins do histograma). A contagem de cores em cada bin ´e usada no c´alculo do histograma. Assim, para cada bin, precisamos analisar os n´ıveis de cinza das 3 bandas da imagem colorida (RGB).

Entendendo Histogramas:

O histograma mostra a frequencia dos valores de brilho da imagem, ou seja, a quantidade de luz presente na imagem.

list_of_images

['Aluminum_01.TIF',
 'Aluminum_02.TIF',
 'Aluminum_03.TIF',
 'Aluminum_04.TIF',
 'Brass_01.TIF',
 'Brass_02.TIF',
 'Brass_04.TIF',
 'Brass_05.TIF',
 'Brass_06.TIF',
 'Brass_07.TIF',
 'Brass_08.TIF',
 'CopperWire_01.TIF',
 'CopperWire_02.TIF',
 'CopperWire_03.TIF',
 'CopperWire_04.TIF',
 'CopperWire_05.TIF',
 'Copper_01.TIF',
 'Copper_02.TIF',
 'Copper_03.TIF',
 'Copper_04.TIF',
 'Copper_05.TIF',
 'Copper_06.TIF',
 'Copper_07.TIF',
 'Copper_08.TIF',
 'Enh_Aluminum_01.TIF',
 'Enh_Aluminum_02.TIF',
 'Enh_Aluminum_03.TIF',
 'Enh_Aluminum_04.TIF',
 'Enh_Brass_01.TIF',
 'Iron_01.TIF',
 'Iron_02.TIF',
 'Iron_03.TIF',
 'Iron_04.TIF',
 'Iron_05.TIF',
 'Iron_06.TIF',
 'Iron_07.TIF',
 'Iron_08.TIF',
 'MASK_Enh_Aluminum_01.TIF',
 'MASK_Enh_Aluminum_02.TIF',
 'MASK_Enh_Aluminum_03.TIF',
 'MASK_Enh_Aluminum_04.TIF',
 'MASK_Enh_Brass_01.TIF',
 'PaintedIron_01.TIF',
 'PaintedIron_02.TIF',
 'PaintedIron_03.TIF',
 'PaintedIron_04.TIF',
 'PaintedIron_05.TIF',
 'PaintedIron_06.TIF',
 'StainlessSteel_01.TIF',
 'StainlessSteel_02.TIF',
 'StainlessSteel_03.TIF',
 'StainlessSteel_04.TIF',
 'StainlessSteel_05.TIF',
 'StainlessSteel_06.TIF']

# Delete values from list - Bad image names
def remove_values_from_list(list_values, mask):
    list_new = list()
    for list_value in list_values:
        if(mask not in list_value):
            print(list_value)
            list_new.append(list_value)
    return list_new 

# Remove from list names with 'MASK'
new_list = remove_values_from_list(list_of_images, 'MASK')

Aluminum_01.TIF
Aluminum_02.TIF
Aluminum_03.TIF
Aluminum_04.TIF
Brass_01.TIF
Brass_02.TIF
Brass_04.TIF
Brass_05.TIF
Brass_06.TIF
Brass_07.TIF
Brass_08.TIF
CopperWire_01.TIF
CopperWire_02.TIF
CopperWire_03.TIF
CopperWire_04.TIF
CopperWire_05.TIF
Copper_01.TIF
Copper_02.TIF
Copper_03.TIF
Copper_04.TIF
Copper_05.TIF
Copper_06.TIF
Copper_07.TIF
Copper_08.TIF
Enh_Aluminum_01.TIF
Enh_Aluminum_02.TIF
Enh_Aluminum_03.TIF
Enh_Aluminum_04.TIF
Enh_Brass_01.TIF
Iron_01.TIF
Iron_02.TIF
Iron_03.TIF
Iron_04.TIF
Iron_05.TIF
Iron_06.TIF
Iron_07.TIF
Iron_08.TIF
PaintedIron_01.TIF
PaintedIron_02.TIF
PaintedIron_03.TIF
PaintedIron_04.TIF
PaintedIron_05.TIF
PaintedIron_06.TIF
StainlessSteel_01.TIF
StainlessSteel_02.TIF
StainlessSteel_03.TIF
StainlessSteel_04.TIF
StainlessSteel_05.TIF
StainlessSteel_06.TIF

# Remove from list names with 'Enh'
new_list = remove_values_from_list(new_list, 'Enh')

Aluminum_01.TIF
Aluminum_02.TIF
Aluminum_03.TIF
Aluminum_04.TIF
Brass_01.TIF
Brass_02.TIF
Brass_04.TIF
Brass_05.TIF
Brass_06.TIF
Brass_07.TIF
Brass_08.TIF
CopperWire_01.TIF
CopperWire_02.TIF
CopperWire_03.TIF
CopperWire_04.TIF
CopperWire_05.TIF
Copper_01.TIF
Copper_02.TIF
Copper_03.TIF
Copper_04.TIF
Copper_05.TIF
Copper_06.TIF
Copper_07.TIF
Copper_08.TIF
Iron_01.TIF
Iron_02.TIF
Iron_03.TIF
Iron_04.TIF
Iron_05.TIF
Iron_06.TIF
Iron_07.TIF
Iron_08.TIF
PaintedIron_01.TIF
PaintedIron_02.TIF
PaintedIron_03.TIF
PaintedIron_04.TIF
PaintedIron_05.TIF
PaintedIron_06.TIF
StainlessSteel_01.TIF
StainlessSteel_02.TIF
StainlessSteel_03.TIF
StainlessSteel_04.TIF
StainlessSteel_05.TIF
StainlessSteel_06.TIF

list_of_images = new_list

path = mypath + '/' + folder + '/'
path

'C:\\Users\\manuel.robalinho\\Google Drive\\UPT_Portucalense\\Trabalho final\\Classificacao_Sucata\\Jupyter_Notebook/imagedata08/'

# HISTOGRAMS
# Print Histograms for all folder images
# list_of_images has all the name files

for x in list_of_images:
    print('Cv2 Histogram for File:', x)
    print_cv_hist(path, x)

Cv2 Histogram for File: Aluminum_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_01.TIF

Cv2 Histogram for File: Aluminum_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_02.TIF

Cv2 Histogram for File: Aluminum_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_03.TIF

Cv2 Histogram for File: Aluminum_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_04.TIF

Cv2 Histogram for File: Brass_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_01.TIF

Cv2 Histogram for File: Brass_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_02.TIF

Cv2 Histogram for File: Brass_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_04.TIF

Cv2 Histogram for File: Brass_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_05.TIF

Cv2 Histogram for File: Brass_06.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_06.TIF

Cv2 Histogram for File: Brass_07.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_07.TIF

Cv2 Histogram for File: Brass_08.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_08.TIF

Cv2 Histogram for File: CopperWire_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_01.TIF

Cv2 Histogram for File: CopperWire_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_02.TIF

Cv2 Histogram for File: CopperWire_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_03.TIF

Cv2 Histogram for File: CopperWire_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_04.TIF

Cv2 Histogram for File: CopperWire_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_05.TIF

Cv2 Histogram for File: Copper_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_01.TIF

Cv2 Histogram for File: Copper_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_02.TIF

Cv2 Histogram for File: Copper_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_03.TIF

Cv2 Histogram for File: Copper_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_04.TIF

Cv2 Histogram for File: Copper_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_05.TIF

Cv2 Histogram for File: Copper_06.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_06.TIF

Cv2 Histogram for File: Copper_07.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_07.TIF

Cv2 Histogram for File: Copper_08.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_08.TIF

Cv2 Histogram for File: Iron_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_01.TIF

Cv2 Histogram for File: Iron_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_02.TIF

Cv2 Histogram for File: Iron_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_03.TIF

Cv2 Histogram for File: Iron_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_04.TIF

Cv2 Histogram for File: Iron_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_05.TIF

Cv2 Histogram for File: Iron_06.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_06.TIF

Cv2 Histogram for File: Iron_07.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_07.TIF

Cv2 Histogram for File: Iron_08.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_08.TIF

Cv2 Histogram for File: PaintedIron_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_01.TIF

Cv2 Histogram for File: PaintedIron_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_02.TIF

Cv2 Histogram for File: PaintedIron_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_03.TIF

Cv2 Histogram for File: PaintedIron_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_04.TIF

Cv2 Histogram for File: PaintedIron_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_05.TIF

Cv2 Histogram for File: PaintedIron_06.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_06.TIF

Cv2 Histogram for File: StainlessSteel_01.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_01.TIF

Cv2 Histogram for File: StainlessSteel_02.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_02.TIF

Cv2 Histogram for File: StainlessSteel_03.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_03.TIF

Cv2 Histogram for File: StainlessSteel_04.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_04.TIF

Cv2 Histogram for File: StainlessSteel_05.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_05.TIF

Cv2 Histogram for File: StainlessSteel_06.TIF
Cv2 Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_06.TIF

# HISTOGRAMS
# Print Histograms for all folder images
# list_of_images has all the name files

for x in list_of_images:
    print('Matplot Histogram for File:', x)
    print_matplot_hist(path, x)

Matplot Histogram for File: Aluminum_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_01.TIF

Matplot Histogram for File: Aluminum_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_02.TIF

Matplot Histogram for File: Aluminum_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_03.TIF

Matplot Histogram for File: Aluminum_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Aluminum_04.TIF

Matplot Histogram for File: Brass_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_01.TIF

Matplot Histogram for File: Brass_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_02.TIF

Matplot Histogram for File: Brass_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_04.TIF

Matplot Histogram for File: Brass_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_05.TIF

Matplot Histogram for File: Brass_06.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_06.TIF

Matplot Histogram for File: Brass_07.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_07.TIF

Matplot Histogram for File: Brass_08.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Brass_08.TIF

Matplot Histogram for File: CopperWire_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_01.TIF

Matplot Histogram for File: CopperWire_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_02.TIF

Matplot Histogram for File: CopperWire_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_03.TIF

Matplot Histogram for File: CopperWire_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_04.TIF

Matplot Histogram for File: CopperWire_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/CopperWire_05.TIF

Matplot Histogram for File: Copper_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_01.TIF

Matplot Histogram for File: Copper_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_02.TIF

Matplot Histogram for File: Copper_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_03.TIF

Matplot Histogram for File: Copper_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_04.TIF

Matplot Histogram for File: Copper_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_05.TIF

Matplot Histogram for File: Copper_06.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_06.TIF

Matplot Histogram for File: Copper_07.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_07.TIF

Matplot Histogram for File: Copper_08.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Copper_08.TIF

Matplot Histogram for File: Iron_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_01.TIF

Matplot Histogram for File: Iron_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_02.TIF

Matplot Histogram for File: Iron_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_03.TIF

Matplot Histogram for File: Iron_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_04.TIF

Matplot Histogram for File: Iron_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_05.TIF

Matplot Histogram for File: Iron_06.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_06.TIF

Matplot Histogram for File: Iron_07.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_07.TIF

Matplot Histogram for File: Iron_08.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/Iron_08.TIF

Matplot Histogram for File: PaintedIron_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_01.TIF

Matplot Histogram for File: PaintedIron_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_02.TIF

Matplot Histogram for File: PaintedIron_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_03.TIF

Matplot Histogram for File: PaintedIron_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_04.TIF

Matplot Histogram for File: PaintedIron_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_05.TIF

Matplot Histogram for File: PaintedIron_06.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/PaintedIron_06.TIF

Matplot Histogram for File: StainlessSteel_01.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_01.TIF

Matplot Histogram for File: StainlessSteel_02.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_02.TIF

Matplot Histogram for File: StainlessSteel_03.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_03.TIF

Matplot Histogram for File: StainlessSteel_04.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_04.TIF

Matplot Histogram for File: StainlessSteel_05.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_05.TIF

Matplot Histogram for File: StainlessSteel_06.TIF
Matplot Hist from file: C:\Users\manuel.robalinho\Google Drive\UPT_Portucalense\Trabalho final\Classificacao_Sucata\Jupyter_Notebook/imagedata08/StainlessSteel_06.TIF

print('Finished')

Finished